Fully Autonomous Neuromorphic Navigation and Dynamic Obstacle Avoidance
Authors: Xiaochen Shang
Luo Pengwei
Xinning Wang
Jiayue Zhao
Huilin Ge
Bo Dong
Xin Yang
Venue: NeurIPS 2025 spotlight ()
Keywords: neuromorphic
bio-inspred algorithm
dynamic obstacle avoidance
navigation
UAV
autonomous robots
event camera
Primary area: neuroscience_and_cognitive_science
TL;DR: A fully autonomous neuromorphic pipeline for tiny autonomous UAV to navigate and avoid dynamic obstacles under various light conditions in less than 2.3 ms with energy consumption reduced to 21% of traditional architecture.

Abstract: Unmanned aerial vehicles could accurately accomplish complex navigation and obstacle avoidance tasks under external control. However, enabling unmanned aerial vehicles (UAVs) to rely solely on onboard computation and sensing for real-time navigation and dynamic obstacle avoidance remains a significant challenge due to stringent latency and energy constraints. Inspired by the efficiency of biological systems, we propose a fully neuromorphic framework achieving end-to-end obstacle avoidance during navigation with an overall latency of just 2.3 milliseconds. Specifically, our bio-inspired approach enables accurate moving object detection and avoidance without requiring target recognition or trajectory computation. Additionally, we introduce the first monocular event-based pose correction dataset with over 50,000 paired and labeled event streams. We validate our system on an autonomous quadrotor using only onboard resources, demonstrating reliable navigation and avoidance of diverse obstacles moving at speeds up to 10 m/s.

Fully Autonomous Neuromorphic Navigation and Dynamic Obstacle Avoidance
Xiaochen Shang', Pengwei Luo', Xinning Wang', Jiayue Zhao', Huilin Ge?, Bo Dong', Xin Yang
Key Laboratory of Social Computing and Cognitive Intelligence
(Dalian University of Technology), Minstry of Education, Jiansu University of Science and Technology, Cephia Al
xcshang06148pp 44gmmi11.......m aeg.ail.com com xinyangddult. edu.cn
Abstract
Unmanned aerial vehicles could accurately accomplish complex navigation and ob- stacle avoidance tasks under external control. However, enabling unmanned aerial vehicles (UAVs) to rely solely on onboard computation and sensing for real-time navigation and dynamic obstacle avoidance remains significant challenge due stringent latency and energy constraints. Inspired by the efficiency biological systems, we propose fully neuromorphic framework achieving end-to-end obsta- cle avoidance during navigation with an overall latency of just 2.3 milliseconds. Specifically, our bio-inspired approach enables accurate moving object detection and avoidance without requiring target recogmition or trajectory computation. Ad- ditionally, we introduce the first monocular event-based pose correction dataset with over 50,000 paired and labeled event streams. We validate our system on an autonomous quadrotor using only onboard resources, demonstrating reliable navigation and avoidance of diverse obstacles moving at speeds up to 10 m/s under different light conditions, with energy consumption reduced to 21 compared traditional architecture.
1 Introduction
The utilization of UAVs across various applications expanded rapidly over the past decade M. Currently, most UAV heavily on external aids such as positioning systems like Global Positioning System (GPS) 2 for localization and ground stations [3] for navigation and dynamic obstacle avoidance, However, such external aid is not feasible in all circumstances as it could be easily jammed
war interfered in multiple scenarios, including dense urban areas [5), caves, zones 6 Therefore, it vital for UAVs to fully perform navigation and dynamic obstacle avoidance external
tasks using only sensors and computing resources onboard, without any dependence signals or infrastructure. Although applicable options are well researched, most solutions are designed for larger platforms but for tiny UAV systems 77.. Ranging sensors like the Li-DAR system could provide accurate positioning information, but are too heavy and power-hungry to be deployed on tiny autonomous systems S Vision-based approach may be an appropriate way for tiny UAVs since. firstly, visual sensors can be both lightweight and power-efficient (9 10 I Secondly, visual algorithms achieve trate-oftth--ue performance in multiple tasks. However, such high performance comes at excessive computational and memory costs. Mainstream approaches like simultaneous localization and mapping (SL.AM) algorithms 12 and object recognition- based trajectory estimation methods [ 14, 15] consume hundreds of megabytes to several gigabytes of memory and hundreds of gigaflops
16 Such high consumption makes tiny UAV autonomy challenging.
Neuromorphic hardwares provide solution to this problem since the asynchronous and sparse nature of their biomimetic data format could exceed the current standard of energy efficiency, computational consumption, and task accuracy, and thus represents a paradigm shift compared to the traditional
Correspongding Author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Ideally, such data structure could lead to data processing method with higher processing speed and lower energy consumption, but contemporary methods treat
computer vision approach [17 18 19.
event data similar to a traditional image frame (20 21 using "event frames" [22 23
and hence fails to fully leverage the inherent sparsity of event streams [26], resulting a performance
24
25
similar to traditional methods. Inspired by the efficiency of biological systems, we observed frogs can accurately localize fast-moving insects while exhibiting significant disregard for stationary objects. Our further anatomical analysis of their visual neural pathways revealed a striking similarity between the frog's 's predatory behavior and the working principles of real-time scene interaction; hence, by leveraging this similarity, a purely neuromorphic dynamic obstacle avoidance approach upproach-mimicking frog visual neurons becomes feasible.
In this paper, we exhibit a fully neuromorphic pipeline. With only one monocular event camera and an inertial measurement unit (IMU), the autonomous UAV could accomplish the navigation task and the dynamic obstacle avoidance task simultaneously, purely with its onboard computing resources without any external aid. the navigation module, the quadrotor uses IMU data to navigate long distances, and by coupling the risual-homing algorithm and event data, the quadrotor employs an SCNN network to mitigate the effects of error drift. To the best of our knowledge, we construct the first monocular event-based pose correction dataset with 50,234 paired event streams, each labeled with its ground truth extrinsic obtained by motion capture system. In the obstacle avoidance module, by implementing our bio bio-inspired algorithm, the quadrotor could suppress the events produced by static objects. With only events generated by dynamic objects preserved, the algorithm bypasses target recognition and trajectory computation steps, directly outputting evasion maneuvers, and reduces the latency of obstacle avoidance to only 2.3 milliseconds. The significantly reduced latency provides UAVs with longer time window for evasion maneuvers, substantially enhancing their performance when encountering high-speed moving objects. The comparative evaluation with other state-of- the-art dynamic obstacle approaches demonstrates the superior performance of our neuromorphic architecture and bio-inspired algorithm. Additionally, we have validated the effectiveness and robustness of our approach in real-world environments through physical flight experiments under different light conditions, with energy consumption reduced to 21 % compared to the traditional structure.
In summary, our contributions to the community include:
A fully neuromorphic framework enabling tiny UAVs to rely solely on onboard computation and sensing for real-time navigation and dynamic obstacle avoidance.
A bio-inspired approach enabling tiny UAVs to accurately avoid dynamic obstacles at speeds up to 10 m/s with a latency of 2.3 milliseconds.
An open-sourced monocular event-based pose correction dataset with over 50,234 paired and labeled event streams.
2 Related Work
2.1 Neuromorphic Control of Quadrotors
While some studies discuss the topic of neuromorphic control on objects like larger robots and robot arms (27) 228 29 the neuromorphic control system on quadrotors remains an underexplored area in research. A Viale et al.
33 proposed the first example of neuromorphic vision-based controller solving high-speed UAV control task by using spiking neuronal network with an Intel Loihi chip 31 Dupeyroux et al. (32 accomplished the task of UAV landing with a 3-layer spiking neuronal network on Loihi, and recently, Paredes-Valll Vallés et al. proposed the first fully neuromorphie vision and control pipeline for controlling an autonomous quadrotor and made the quadrotor successfully take off, fly along given route, and then land [33]. The study of neuromorphic control of quadrotors is highly restricted by the hardware performance of embedded neuromorphic processing platforms
34 331 in terms of the number of available neurons and synapses. The Intel Kapoho Bay with Loihi chips [1I] carries 262,100 neurons [30], and the SpiNNaker(SNN architecture) version [36]
has 768,000 neurons. Though higher-neuron neuromorphic platforms expand computational capacity,
they remain inadequate for tasks like optical flow estimation (requiring > 3.7M neurons [37] ). In this
work, we use Speck (38), a neuromorphic SoC (System on Chip) with 327,000 neurons (39 that
could support at most 8 layers of SNNs.
2

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2.2 Visual-homing Algorithm
Visual-homing comes from the idea that small insects such ants and bees can navigate long distances despite their tiny brains. The mechanism behind such behavior can be categorized into two parts: path integration and drift error climination. Cartwright and Collet [40] first proposed a snapshot model that describes the homing behavior of bees, and researchers in the field of robotics use this
concept to develop efficient navigation algorithms for tiny robots + 42 Subsequent researches focus on reducing the memory required for first is the reduction of the memory consumed by snapshots: Stürzl and Mallot snapshot into the frequency domain and remembered only the lowest-frequenc component, and reduced the size of the snapshot remarkably. The second direction to increase the spacing between snapshots. Denuelle and Srinivasan [44] proposed study that uses the homing vector position estimate relative to the snapshot, enabling the drone to navigate some distance toward the next snapshot area. Van Dijk al. [45] combined two directions and successfully deployed visual-homing on a tiny 56-gram autonomous drone with one panoramic camera. For detailed biological concepts, please take a look at the supplementary note 3.
and has been made in two directions. The
45] transformed the
2.3 Frog-eye Receptive Field
In nature, frogs' visual systems exhibit high-fidelity motion detection for fast-moving objects with deliberate suppression of static background stimuli. The observed motion selectivity stems from specialized receptive field organization in the anuran retinotectal system [46] 47). During the past decades, researchers conducted extensive research on such mechanism and found that R3 ganglion cells respond to stimuli to ON-OFF brightness changes, create motion-sensitive detection zones [48] 49 (0). In the standard model of such detection zones, ERF (excitatory receptive field) and IRF (inhibitory receptive field) generate symmetrical excitatory and inhibitory responses to ON-OFF stimuli. Extending these findings, Hoshino al. [5T] identified functional asymmetry in the spatial organization of ERF and IRF. For detailed biological concepts, please take a look at the supplementary note 3.
2.4 Dynamic Obstacle Avoidance
The dynamic obstacle avoidance problem for unmanned aerial vehicles has been widely researched in recent years, but mainly in the aspect of quasi-static environment [52 and low-speed obstacles. Even though existing literature that relies monocular vision [53 54 55 14] stereo vision [56 57 and
depth camera [13 58 59] exhibits satisfactory performance on slow-moving objects like pedestrians [60], their performance dealing with high-speed dynamic obstacles like thrown balls, birds[6]] and even other unmanned aerial vehicles cannot meet the requirement of real-time avoidance. However, despite Falanga et al. [26] displaying the concept of using event stream directly, many researchers still treat events the form of "event frame" (23]. To the best of our knowledge, this is the first work that implements low-latency (2.3 milliseconds) dynamic obstacle avoidance when the quadrotor is executing a navigation task without the help of any external infrastructure.
3 Methods
In this section, we introduce our neuromorphic navigation and dynamic obstacle avoidance pipeline, which includes neuromorphic control framework that allocates computing resources to minimize evasion latency and maximize navigation correctness, an ceant-viuallhomming based end-to-end method and a bio-inspired dynamic obstacle avoidance algorithm that reduces the latency of obstacle detection to 2.3 milliseconds and applicable for dodging multiple high-speed obstacles when the drone navigating to its destination.
3.1 Overview of Neuromorphic Control
The neuromorphic control framework is implemented on the Speck Neuromorphic SoC [38 and deployed on small quadrotor for navigation and dynamic obstacle avoidance. The schematic of the quadrotor is illustrated in Fig. 2 In this framework, we assume the quadrotor first performs an outbound flight towards a designated target, which could be under any control law, including manual control, and then performs an inbound navigation and avoids multiple dynamic obstacles during this navigation in a fully autonomous fashion. Since our focus on the navigation and dynamic obstacle avoidance during the inbound flight, we assume the outbound flight is performed without
3

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Obstacle
Obstacle
Dynamic
Obstade
8
Figure I: Overview of the whole task. During the outbound flight, which could be under any control law (1) and periodically records event stream (2). During the inbound flight, the quadrotor uses IMU information to travel to the location near next snapshot point and avoid any dynamic obstacles during its flight (3), and then records event stream continuously to recalibrate it's position (4) until the distance to the snapshot point is smaller than the threshold (5). These steps repeat until the quadrotor reaches its destination.
any collision, the environment is static (surroundings don't 't change), and no dynamic obstacles appear when the quadrotor is recalibrating the drift error of the IMU.
To minimize the obstacle avoidance latency under strict computational resource restriction 38 for longer response windows and higher success rates, we need to minimize the resources used for navigation. By introducing visual- visual-homing -homing, navigation during most of the flight is accomplished solely by odometry with negligible computational overhead, thereby reserving sufficient resources for obstacle avoidance. Moreover, both the calibration phase and obstacle avoidance module share the same monocular event camera, which not only reduces computational load but also significantly decreases the UAV's payload, ultimately enhancing its motion performance.
3.2
Event Visual-homing
IMU During the outbound flight, the quadrotor records all IMU information it produces without any correction. Generally, the IMU drift error stacks over time and will gradually become too large
Neuromorphic Framework
Mosion Pramowork
Event Strom
(MI
splos
Mevement
Figure 2: Schematic of the neuromorphic quadrotor system The left part is the quadrotor used in this work, total weight of 856 g, tip-to-tip diameter 240 mm, with the numbers indicating the components in the right part. The right part is the hardware overview with the display of data flow, with components divided into two frameworks: the neuromorphic framework and the motion framework. One for processing neuromorphic data and the other one processing the movement control.

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Absolure
30
Culbration
Snapshot Zone
Snapshot Evant Tensor
Shared Weights Extractor
Rei
OG
Oec
Inttial Pos.
Capture
Calibration
Ay
Inbound Capture
CalibNet
MoveCmd
Calloration Evantt Tensor
Inbound
Ezt
Fet
Eas
MovaCmd 2
Capture
Figure 3: Illustration of Visual-homing and CalibNet.The quadrotor continuously calibrates itself in the catchment until reaches the snapshot zone. The catchment area is defined circular region with a 60cm radius, within which the quadrotor must remain positioned when initiating the calibration process. The snapshot constitutes smaller 20em-radius circular centered within the catchment area; calibration terminates once the quadrotor enters this central zone.
to provide applicable navigation information for the quadrotor[C) 62 stack-over-time enror can be defined as follows:
simplified version of the
NrN = ôrN.0 + ôuN,o.t + 5 d0, + baN) [g by
(1)
where dr is the initial position error, remains the same for all time; SUN e is the initial velocity error with linear amplification; g 50, denotes initial attitude angle error, baN denotes accelerometer error, and the third term exhibits quadratic divergence; g byg denotes angular velocity error, and the last term exhibits cubic divergence. As shown in the formula, the IMU error exhibits upproximately cubic drift over time. We let the quadrotor use risual-homing algorithm to periodically return to snapshot positions, and recalibrate IMU drift error before the error aggregates too high. After the homing, the only error the homing error during the recalibration process.
Event-based Drift Error Recalibration Network As we have shown in formula 1, we can periodically recalibrate the error caused by drift before it becomes too large, thereby keeping it at relatively small level consistently. By quantitatively analyzing IMU eπror propagation, shown in supplementary material 8, we can estimate the maximum potential position drift the quadrotor, and set the calibration interval to value that guarantees the quadrotor will remain within the catchment area. To fully utilize the advantage of event data and a neuromorphic framework, we use an SCNN (spiking convolutional neural network) with Siamese structure [63] The feature extractor of the network extracts features from two continuous event streams with a temporal time window of 50 ms. The first event stream is filmed at snapshot position during the outbound flight, and the second event stream is filmed near snapshot position during the inbound flight. Two feature tensors are then concatenated and passed to the calibration module. Finally, the network outputs vector containing the relative x, coordinate differences and the yaw angle difference between two captured points, as illustrated in Figure.
To solve the scale issue which makes the network impossible to determine the absolute scale of object brought by the monocular event camera, train the network using data obtained in similar-scale environment and design cyclic correction method where the UAV continues capturing event stream from the corrected position and performs repeated correction until the position error output by the network falls below a specific threshold. Training details are shown in supplementary note 6.

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(A) Raw Event
(B) ERF
(C) IRF IRF
(D) Event RF
(E) Raw PF
(F) Diffused
Figure 4: Illustration of the workflow of the Event RF Model and the potential field movement command generator. The UAV avoids dynamic obstacles' high-energy zones via the gradient descent method. The red dot the representation of the quadrotor and the length and direction of the red arrow represent the moving direction and speed of the quadrotor.
Dataset To address the absence of benchmark data for monocular event-based pose correction, we constructed novel dataset containing 50.234 event stream pairs, each precisely annotated with 4-DoF relative pose (Ar, Ay, A=, AΦ) ground truth. There are four distinct indoor scenarios contained in the dataset, and maximum object-camera proximity constrained to 10-meter range. The camera was mounted on a DJI Ronin SC gimbal (++0.02 stabilization accuracy) during shooting, which eliminated the influence of pitch and roll angles while simulating the stabilized attitude of drone equipped with flight controllers. The ground truth of camera's 's shooting position is obtained by a motion capture system with 12 Vicon Vero 2.2 motion capture camera, each featuring with resolution of 2048 1088 and max frame rate of 330 Hz.
3.3 Bio-inspired Dynamic Obstacle Avoidance
Event Receptive Field Model The nrightness-sensittiee biological mechanism behind anuran ganglion cells exhibits isomorphic correspondence with event-based vision sensing. By leveraging the ERF-IRF spatial asymmetry, we proposed an Event RF (receptive field) model, used for suppressing the event stream produced by static objects and background, and enhancing the event stream produced by dynamic obstacles:
F(x, y/ en; = min( A K(1, To Gar, y, en), Em) minn ( AI, TT) y, en), Jeh) (2) where A, is ERF parameter, A2 is IRF parameter, T is energy decay parameter, At is IRF delay parameter, and K(t,7) r) is time kernel function:
t/T
K(t,
0
t <<0 0
and the first term min(AiK(e (L, GT, y, en), Eaa) the ERF energy level, while Es is ERF energy threshold, the second term min(A, (A, At, e,), In) is the IRF suppression level, while Ith is the IRF suppression threshold.
en is the event passed to the model, with its coordinates and timestamp as (In, Vn, tn). G(z, en) the Gaussian kernel function:
1
xT
ya)
G( y, = 2TO exp(
20
20
where a, and By are standard deviations along the major and minor axes of the 2D elliptical Gaussian function.In In this model, the event stream from static objects is quickly suppressed by the IRF, which drives the energy level close to zero. In contrast, the event stream produced by moving objects resists suppression, allowing it to maintain high energy level persistently, as shown in Fig. 4D 4D The demonstration the equivalence between the dynamic and static event selection mechanism of Event RF model and the receptive field mechanism of the frog eye can be found in the supplementary note
Potential Field Based Movement Command Generator We proposed a potential
method to generate movement commands from the processed event stream obtained from the Event RF Model. By converting the energy map directly to the activation map, we can consider the event camera's field of view as 2-dimensional plane and construct potential on this plane based on the energy level of the event stream, shown in Fig. 4E After removing the points with excessively low

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potential in this potential field (here we set the threshold as half of the maximum potential), we can consider that the potential field fully represents the moving obstacles within the event camera's 's field of view, as shown in Fig. 4F
Since we are using a monocular event camera the only sensor to capture the dynamic obstacle, the depth information of the dynamic obstacle cannot be obtained, which means obstacles far from the quadrotor can also be considered dangerous objects that need to be avoided. To solve this, we use the Two-Pass Algorithm to make a connected component detection. Neglecting the potential level of points, we consider points with potential and points without potential as O, and convert the map to binary image I:
I = (I( E ( H, si W}
(5)
By making the connected component detection, we can assess the danger level of dynamic obstacles based on the proportion of their potential field regions occupying the entire field of view. For those dangerous potential field clusters, we define dilation function:
(I ④ B) y) = max [(x + (y + +) for (i.j) E B
where @ is the symbol of dilation operation, (z, y) is the coordinate of the point in the plane surface, (i,j) is the offset the structuring element B.
After the dilation process, since we can consider the position of the quadrotor in the center of the potential field map, we can now determine the motion direction and motion intensity of the quadrotor using gradient descent in the artificial potential field.
4 Experiments
4.1 Simulation Experiments
Before combining visual navigation and dynamic obstacle avoidance into a single neuromorphic system, we conducted separate experiments to verify the effectiveness of each part. The whole simulation experiment is in the Gazebo simulation environment.
Visual-homing Navigation We trained the Siamese Network using the dataset we constructed, and we test the navigation process in three different customized maps, with difficulty from low to high. The flight distance is 40 meters for the easiest map and 130 meters for the other two maps.
Fig. shows the resulting trajectories for the proposed method. The quadrotor successfully and steadily followed the route of outbound flight and reached the starting point. Based the calculated drift error propagation, we set the snapshot interval at 7.5 meters, with each snapshot occupying 240 KB of storage space. We conducted repeated experiments to analyze the propagation degrees of X-axis translations, Y-axis translations, and yaw angle errors. By obtaining these data, we ensured that under our setting of calibration interval, the quadrotor's 'ss positional drift remains strictly bounded within the designated 60cm- radius catchment area. Details on IMU error analysis can be found in supplementary note 8. We test the navigation procedure 10 times in each map, and in every single test, the quadrotor successfully reaches the destination.
Dynamic Obstacle Avoidance We use ESIM [5 an event camera simulator in Gazebo, to simulate event camera imaging effects for the quadrotor and conducted 300 dynamic obstacle avoidance tests. The dynamic obstacles were categorized into three groups based on size: coin-sized, tennis ball-sized, and basketball-sized Each group was tested 25 times at four different distance ranges: within 0.2 0.5m. 0.5-Im, 1-2m, and beyond 2m. We set the closest starting distance of obstacles at 0.2m since firstly, if the obstacle is too close to the quadrotor, the entire field of view will be occupied by the dynamic obstacles and the algorithm cannot make effective obstacle avoidance commands, and secondly, in real-world scenarios, it is generally impossible for dynamic to abruptly appear within the drone's immediate proximity.
For each obstacle detection, we also marked its centroid in the image frame and compared it with the centroid of the algorithm-processed processed event stream to validate the position error in dynamic obstacle detection, as shown in Table Details about the calculation are provided in supplementary note 4.
To quantify the computational cost of the model, we recorded multiple event streams of dynamic obstacles and processed these event streams with our algorithm to calculate the processing time to evaluate the delay of our model. Since the model relies on generating IRF fields from prior processed events and applying decay on both ERF and IRF fields, biased results inevitably arise when processing arbitrarily cropped sections of the raw event stream. We use our algorithm to process the whole event
7

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Table 1: Centroid Difference Between RF Model and GT (m)
Obstacle Type
Distance
Mean
Median
Std. Dev.
M.A.D
SR
0.2m 0.5m
0.0179
0.0164
0.0089
0.0015
94%
0.5m 1m
0.0177
0.0134
0.0094
0.0008
92%
Coin-sized
Im 2m
0.0138
0.0126
0.0077
0.0202
90%
2m+
0.0132
0.0123
0.0053
0.0042
86%
0.2m 0.5m
0.0213
0.0228
0.0093
0.0052
92%
0.5m 1m
0.0180
0.0169
0.0072
0.0093
98%
Tennis-sized
Im 2m
0.0168
0.0146
0.0095
0.0002
100%
2m+
0.0129
0,0102
0.0103
0.0057
96%
0.2m 0.5m
0.0306
0.0297
0.0141
0.0017
84%
0.5m Im
0.0271
0.0261
0.0135
0.0026
96%
Basketball-sized
Im- 2m
0.0196
0.0196
0.0059
0.0081
100%
2m+
0.0222
0.0169
0.0166
0.0109
100%
stream and compute the ratio between the processing time and the total length of the event stream to obtain the unbiased average latency of 2.3 ms. Detailed data are shown in supplementary note 11.
Combined Task Simulation With the core algorithms proven, we then demonstrated the complete pipeline by combining the complex tasks together. Using the 3 maps we created in Gazebo (mentioned in 4.1.1), we made the quadrotor traverse the outbound route using odometry without any global position information and added randomly throwing dynamic obstacles when the quadrotor is flying during its inbound journey. Among tests on each map, the success rate is 100% for the first map, 98% for the second map, and 94% for the last map. Figure demonstrations and other details are shown in supplementary note 7.
4.2 Real-world Experiments
In this section, conduct indoor experiments using our neuromorphic platform mentioned in section 3.1, and also conduct extra indoor experiments under different extreme conditions (flicker condition and darkish condition) to validate the algorithm's 's robustness.
Indoor Experiment As previously mentioned, the main goal of the indoor experiment is to verify the effectiveness of our neuromorphic framework in real-world setup and test our neuromorphic structure's advantage in computa- tional resource consumption, energy consumption, and verify the performance of the framework with such low consumption in a tiny autonomous quadrotor. The exper- iment is conducted in a 10m * 10m flight arena. Three experimenters stationed at designated locations threw dy- namic obstacles at passing drones, and experimenters were instructed to remain stationary prevent the quadrotor from misidentifying them as dynamic obstacles. During 10 repeated trials, the quadrotor successfully avoided all dynamic obstacles and reached the destination in every instance, as shown in Fig. 5
Trajectary the dynamic obstacle Trajectory the quadiretor
Figure 5: Real-worid Experiment The quadrotor avoids the tennis ball thrown by experimenters during navigation.
Details about
obstacles are shown in supplementary note
Complex Environment Experi- ments We conducted additional experiments in both outdoor (a square with static boxes) and cluttered indoor environments (office corridors), test- ing three dynamic obstacles: thrown objects, sparse crowds, and dense crowds. Results show consistent per- formance across environments, with outdoor lighting/airflow variations causing no significant impact. The system maintained high navigation
Table 2: Quantitative evaluation on Outdoor Environ-
ments and Cluttered Indoor Environments
Scenario
Obstacle Type
SR
Thrown objects 94.6%
Office building corridor
Sparse pedestrians
93,8%
Dense pedestrians
62.1%
Thrown objects
94.1%
Outdorr square
Sparse pedestrians
93.7%
Dense pedestrians
54.8%

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success rates for thrown objects and sparse crowds, with performance degrading only in extreme crowd densities.
Reduced Energy Consumption on Neuromorphic Hardware We tested the energy consump- tion and run time between different setups, and main observation is that the neuromorphic chip demonstrates a two orders of magnitude reduction in power consumption compared to conventional devices. Systems equipped with this neuromorphic chip achieve a 95% reduction in operational energy consumption (down to 5% of original levels) when executing identical tasks using the same algorithms. The total system energy consumption decreases to 21 % of baseline values. Notably, in neuromorphic systems, the primary energy expenditure originates from three core processes: the onboard computer operations, data exchange between the neuromorphic chip and flight controller, and motion command execution. Details about the energy consumption of each architecture are displayed in supplementary note 10.
Robustness Under Extreme Light Condition Despite our main goal is to validate the advantage of our bio-inspired algorithm and fully neuromorphic framework, to better demonstrate the effectiveness of the unique event stream data modality, we need the framework under extreme light conditions to prove its robustness. We test the flight performance in the same arena under three different light conditions: light (10- 100 lux), flicker (1 100 lux), dim (1 10 lux), and dark (0 1 lux). The result shows that the performance of the quadrotor is approximately the same under different light conditions, but does not work in dark conditions. Experiment details are shown in supplementary note 9.
4.3 Comparison and Analysis
Comparison with the State-of- the-Arts Since this work is the first to implement a fully neu- romorphic pipeline complex navigation and dynamic obsta- cle avoidance tasks, to provide reference level, we compare our Event RF model some related traditional approaches based on
Table 3: Quantitative evaluation on Dynamic Obstacle Avoid- ance Task Only Method 4 utilized non-visue non-visual sensors, and experi- ments that did not employ onboard computational resources were specifically marked.
Method
Latency
Pos. Err.
SR
Method 1
64
19. 12 ms
0.11 m
96.3
Method 2
13
39.49 ms
0.11 m
89.1
object recognition and trajectory
Method 3 [26]
3.56 ms
0.09 m
86.7
estimation 64 13
26
651, as
Method 4 (GTX 4090) 665
14 ms
LiDAR
95.75
shown in Table 3 These ap-
Method 4 (onboard) 65
27 ms
LiDAR
86.5
proaches are tested under the Ours
2.34 ms
0.02 m
94.5
same simulation environment us-
ing their official codes. Since Li-DAR can get precise position information of obstacles, there is no position error for the Li-DAR method. Among all the works, our system achieves the lowest latency, with 88.6% reduction compared to the average latency of other methods. The only approach with comparable latency is the method 3 226), which also employs an event camera but lacks a navigation module, thus allocating all computational resources to obstacle avoidance. Since our method does not perform object recognition or trajectory estimation, we cannot compare prediction speed errors. However, in terms of positional error, work also achieves the lowest. Regarding obstacle avoidance success rate, our performance is very close to the best, with only a 2% gap.
Analysis of Event RF Model We delve into the parameter choosing for the Event RF model and conduct simulation ex- periments on multiple dynamic obstacles of different sizes to evaluate the effect of parameter selection on performance and the generalizability of the parame- ters, as shown in Table. 4 There are 3 pairs of parameter in Event RF model: A2); (Te; Tı), (Esh, Inn), and 3 sepa- rate parameters: Át, Tn, Oa, Ty. and the value of each parameter significantly affects the model's performance.
Table 4: Model Performance with Fixed Parameters vs. Obstacles at Various Speeds While the model ex- hibits general robustness across range of velocities, task-specific parameter tuning can yield superior perfor- mance in dedicated scenarios.
Type
SR
Low-speed (2m/s)
100%
High-speed (8m/s)
100%
Ultra-high-speed (15m/s)
70%
Ultra-high-spen (specified parameters)
90%

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Zz: Ty. as the standard deviation along the major and minor axes of the 2D elliptical Gaussian function, affects the size of the receptive field generated by each event. Under perfect motion compensation, Øx = oy = makes the IRF just sufficient to suppress the stimulation caused by the ERF. However, considering the limited computational resources, achieving perfect motion compensation is challenging, along with the inherent noise introduced by the event camera itself. Setting oN = By = 2 could achieve a better result. Making oN and By unequal could enable the model to exhibit anisotropy, reducing sensitivity to motion in specific directions, especially when setting different σ₂ and By for ERF and IRF individually.
At affects the delay of the IRF relative to the ERF. Higher At increases the size of high-energy regions for dynamic objects in the model, thereby increasing the distance between the centroid of the real obstacle and the centroid of the dynamic obstacle in the model, resulting in greater error. However, if át is too small, the ERF will be rapidly overridden by the IRF, thereby reducing the model's sensitivity to slow-moving objects. In experiments, we find At 5 ms delivers optimal performance, and this value suitable for the vast majority of dynamic obstacle avoidance scenarios.
Through mathematical derivation, we found that the model achieves optimal performance when A = e -1/1,, the model reaches optimal performance, and since Te < T; we can determine the values of A1, A2 and Te based on the value of т;. In biological systems, the value of ri typically ranges from 25 50 ms. We conducted tests at 5-millisecond (ms) intervals and found the best value as 25 ms. Therefore, AL should be 1.22, and when Te ms we get the best result. Under ideal conditions, setting Σst- Lth would enable perfect static event cancellation. However, in practice, sensor noise and firing threshold fluctuations in biological neurons necessitate permitting minor deviations to prevent noise-induced false dynamic responses; here we choose our experimental testing. Details on the analysis process can be found in supplementary note 12.
A
As
iaa Esa = 1.2 based on
5 Conclusion
This paper presents a fully autonomous neuromorphic navigation and dynamic obstacle avoidance pipeline for tiny autonomous unmanned aerial vehicles. Its Event RF Model is the first bio-inspired algorithm that could make the quadrotor bypass the object recognition and trajectory estimation processes, thus avoiding dynamic obstacles in real-time manner. By reducing the latency to 2.3 ms, the model gives a much longer reaction time window for the quadrotor when facing dynamic obstacles with speeds up to '0m/s.Comparativ evaluations under identical experimental conditions prove our neuromorphic approach outperforms current state-of-the-art solutions for autonomous UAVs, delivering significantly lower latency in high-velocity dynamic obstacle avoidance while maintaining comparable success rates under stringent onboard computational constraints. Moreover, with reduced energy consumption and robustness under various light conditions, this work presents substantial step toward neuromorphic sensing and controlling for UAVs, and exhibits the great potential of neuromorphic architecture on tiny autonomous robots, revealing the possibility of tiny autonomous robots to evolve to higher levels of operational capability and performance.
Broader Impacts and Safeguards While this work on autonomous drones aims to benefit applica- tions like search and rescue in GPS-denied environments, acknowledge its dual-use potential. To mitigate risks such as privacy invasion and malicious payload delivery, our approach integrates key safeguards. Primarily, the use of an event cahera-which captures only illumination changes rather than identifiable imagery-rovvides an inherent layer of privacy protection by design. Furthermore, our open-source license and code documentation explicitly prohibit harmful misuse. These measures help ensure the technology's 's responsible development and deployment.
Limitation and Future Work The current work relies on IMU information for navigation, and the monocular event camera could not obtain depth information of the dynamic obstacle. Future work will further explore the Event RF Model's 's capabilities by leveraging its ability to distinguish between dynamic and static objects. A stereo vision setup will be used to explore the possibility of tiny autonomous neuromorphic quadrotors exploring and avoiding dynamic obstacles in completely unfamiliar environments without relying on any prior information.
Acknowledgments and Disclosure of Funding
The study has been supported by National Key Research and Development Program of China (N.20022Z00200500) and Ningbo Major Research and Development Plan Project (No. 20232223...
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Reviews:
[{"review_id": "v6Vbf7fOWg", "reviewer": "Reviewer_ugNt", "summary": "The work proposes to exploit event camera for drone navigation and dynamic obstacle avoidance, for visual-homing task. In particular, the visual-homing task is that after they fly the drone away the home position (using whatever method, e.g., manual or autonomous), they asked the drone to flying back to the home position. \n\nSince they only use IMU measurements for trajectory estimation, they propose to exploit event data for drifting correction. The main insight is that they propose a snapshot zone (fig. 3) along the path and captures the event stream during the outbound flight and inbound flight, and exploit the two event stream segment to predict a delta motion to correct the IMU integrating drifts. \n\nAnother contribution is that they propose an Event receptive field to detect dynamic objects. However the way to exploit this receptive field for object avoid is not clearly described.", "questions": "N.A.", "limitations": "N.A.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "## Strengths ## \n1. It is interesting to explore event camera for robot navigation and dynamic obstacle avoidance;\n2. They also propose a method to detect dynamic objects by using event stream;\n\n## Weaknesses ##\n1. The paper is hard to follow, not because the language, but because the logic behind the method. In particular: the reviewer agrees that it might be possible to exploit both outbound and inbound event stream segment to predict the relative position change. However, how can the drone know it reaches the snapshot zone if it utilize the integrated position from IMU measurements only (large position errors)? If the drone could not identify the snapshot zone, the captured event stream segment would have no overlap. Then how the network can be used to predict the position change between the outbound flight and inbound flight. \n\n2. The paper also lacks an overview description of the method such that the readers could understand how different components are interacting with each other to achieve the final goal.\n\n3. How the potential field is computed and how it is used to avoid dynamic obstacles exactly?\n\n4. From the first principle, if the navigation relies mainly on IMU measurements. The reviewer think the method could not work at all for real hardware because the estimated position from IMU could drift away quickly. \"In line 154, the authors mention tha the most of the flight is accomplished solely by odometry with negligible computational  overhead...\"\n\n## \nBased on the rational and lack of details of the paper, the reviewer gives his current rating. If there is any misunderstanding, the reviewer is open for further discussion and could upgrade his rating accordingly.", "was_revised": true, "final_justification": "The rebuttal has addressed my concerns. I am happy to increase the final ratings accordingly.", "rebuttal": "We truly appreciated your sugestion and we sincerely appreciate your recognizing our event camera-assisted navigation approach as \"*interesting*\" and your recognition of our Event-RF Model, as decribed by reviewer 9Vrp as \"*novel*\" and \"*interesting and creative solution*\", by reviewer Beb7 as \"*quite original*\" and \"*offering a promising path forward*\" and by reviewer ZD1j as a *\"novel approach.\"*\n\n>**Q1: The paper is hard to follow, not because the language, but because the logic behind the method. In particular: the reviewer agrees that it might be possible to exploit both outbound and inbound event stream segment to predict the relative position change. However, how can the drone know it reaches the snapshot zone if it utilize the integrated position from IMU measurements only (large position errors)? If the drone could not identify the snapshot zone, the captured event stream segment would have no overlap. Then how the network can be used to predict the position change between the outbound flight and inbound flight.**\n\nWe sincerely appreciate this insightful observation. While such scenarios could theoretically occur if the algorithm were run without any design of calibration interval setting, we have proactively addressed this through carefully designed limitations on position correction sampling—as explained in the main text (Line 244: *\"Based on the calculated drift error propagation, we set the snapshot interval at 5-10 meters...\"*) and detailed with experiments in supplementary material (Supplementary Note 8).\n\nTo completely eliminate the possibility of the scenario you described occurring in our experiments, we quantitatively analyzed IMU error propagation to estimate the quadrotor's maximum potential position drift. As described by Formula 1, IMU errors approximately propagate with the cube of time. We conducted repeated experiments to analyze the propagation degrees of X-axis translations, Y-axis translations, and yaw angle errors. By obtaining these data, we ensured that under our setting of calibration interval (7.5m in our context), the quadrotor's positional drift remains strictly bounded within the designated 60cm-radius catchment area.\n\nRegarding catchment area and snapshot zone, the catchment area is defined as a circular region with a 60cm radius, within which the quadrotor must remain positioned when initiating the calibration process. The snapshot zone constitutes a smaller 20cm-radius circular area centered within the catchment area; calibration terminates once the quadrotor enters this central zone. The 60cm radius of our catchment area was empirically determined through extensive network testing, which demonstrated that this spatial boundary reliably produces low-error outputs. Furthermore, each calibration procedure includes an additional verification step - the calibration cycle only completes when the secondary calibration predicts a positional offset below 20cm, indicating the network's confidence that the quadrotor has effectively reached the snapshot zone. \n\nOur network utilizes an end-to-end Siamese CNN (SCNN) architecture that takes two accumulated event streams as input: (1) a reference event stream captured at the exact center of the snapshot zone during the outbound route, and (2) a calibration event stream obtained during recalibration. (Line 176: *\"The first event stream is filmed at snapshot position...\"* and Supplementary Note 6) Through experiments of IMU error propagation, we confirmed the calibration event stream is obtained within the defined catchment area. The network directly outputs the relative x/y translations and yaw angle difference between these two viewpoints for IMU drift correction.\n\nWe will clarify this fact more clearly in the camera-ready version.\n\n>**Q2: The paper also lacks an overview description of the method such that the readers could understand how different components are interacting with each other to achieve the final goal.**\n\nThank you for your suggestion. We agree that although the supplementary material (Figure 10) outlines the overall task workflow and module interactions, the main text would benefit from a more detailed system overview. We have now incorporated a new figure and a comprehensive description of the complete system architecture and will integrate this enhancement into Section 3 of the camera-ready version.\n\nThe system can be simply divided into two different parts, as stated by Reviewer 9Vrp: *\"a bio-inspired \"Event RF Model\" for fast, low-latency detection and avoidance of moving obstacles, and a visual-homing algorithm that employs a Spiking Convolutional Neural Network (SCNN) to correct for IMU drift over long trajectories.\"*\n\nWithin our neuromorphic architecture, the quadrotor's onboard computational resources have been minimized to an extreme degree (utilizing only a single low-power neuromorphic chip), necessitating an ultra-efficient IMU-based navigation approach supplemented by periodic SCNN-based drift correction at fixed intervals. For dynamic obstacle avoidance under these stringent computational constraints - where conventional recognition-and-trajectory-prediction methods remain infeasible despite the IMU's minimal footprint - our biologically inspired Event-RF Model achieves real-time performance (2.3ms latency) through a novel reactive avoidance paradigm.\n\n>**Q3: How the potential field is computed and how it is used to avoid dynamic obstacles exactly?**\n\nThe potential field can be decomposed into two components: the raw potential field and the diffused potential field (Figure 2). As we mentioned in line 215: *\"By converting the energy map directly to the activation map...\"*, the raw potential field is directly derived from the output of our Event-RF Model. As mathematically described in the Event-RF Model (Formula 2), the Event-RF model's energy representation is fundamentally characterized by the superposition of positive energy from the ERF field and negative energy from the IRF field. This unique formulation ensures that all dynamic object events filtered by the model inherently carry high energy signatures. The resultant energy field can be directly transformed into a potential field.\n\nThe diffused potential field, as illustrated in Figure 2F, is generated by processing the raw potential field through our Two-Pass algorithm, as described in line 223: *\"To solve this, we use the Two-Pass Algorithm to make...\"*. The raw potential field cannot be directly used to generate movement commands because the potential levels between individual points lack correlation, resulting in an absence of energy gradients within the field. To address this, we employ the Two-Pass algorithm (Formula 5) to remove low-energy points while filtering out small high-energy regions by connected component detection (considered distant obstacles or noise). Subsequently, we diffuse the remaining high-energy points (Formula 6), thereby transforming the raw potential field into a diffused potential field with computable gradients. These gradients then form the basis for calculating the quadrotor's avoidance actions.\n\nIntuitively, as illustrated in Figure 2F, since the 2D potential map can be considered the projection of the event camera's field of view, with the red dot (centered at the diffused potential field) representing the position of the quadrotor. When an obstacle appears, a corresponding high-potential region will emerge in the diffused potential field. This causes the red point (representing the quadrotor) to naturally move away from the obstacle along the gradient descent direction, as indicated by the red arrow, which simultaneously defines its avoidance trajectory.\n\n>**Q4: From the first principle, if the navigation relies mainly on IMU measurements. The reviewer think the method could not work at all for real hardware because the estimated position from IMU could drift away quickly. \"In line 154, the authors mention that the most of the flight is accomplished solely by odometry with negligible computational overhead...\"**\n\nOur real-world experimental results demonstrate that our approach functions effectively with real hardware (Section 4.2 and the demo video). As mentioned in the response to Q1, our comprehensive approach - combining IMU drift rate analysis, calibrated correction intervals, and optimized catchment area dimensions - systematically prevents potential event stream non-overlapping scenarios, and ultimately confines the IMU drift error to a sufficiently low level to maintain algorithmic effectiveness, thereby eliminating uncontrolled positional deviations.\n\nMore importantly, in practical applications, our method can be modularly integrated with most existing navigation and exploration algorithms. By deploying the navigation algorithm on the quadrotor's onboard computer and running our obstacle avoidance module on neuromorphic chips, the system achieves simultaneous precise navigation and highly-efficiency obstacle avoidance. To validate this capability, we integrated the Event-RF Model with multiple navigation/exploration algorithms and tested them in completely unfamiliar dynamic environments, each with 20 trials and 140 dynamic obstacles. We will include this additional experiment in the supplementary material of the camera-ready version.\n\n|Method|Dynamic obstacle avoidance SR|\n|-|-|\n|EGO-Planner [1]|86.4%|\n|MAVRL [2]|90.0%|\n|NAVRL [3]|94.3%|\n\nReferences\n\n[1] Xin Zhou, Zhepei Wang, Hongkai Ye, Chao Xu, and Fei Gao.  Ego-planner: An esdf-free gradient-based local planner for quadrotors. IEEE  Robotics  and  Automation  Letters, 6(2):478– 485, 2020\n\n[2] Yu, Hang, Christophe De Wagter, and Guido CH E. de Croon. \"MAVRL: Learn to fly in cluttered environments with varying speed.\" IEEE Robotics and Automation Letters (2024).\n\n[3] Xu, Zhefan, et al. \"Navrl: Learning safe flight in dynamic environments.\" IEEE Robotics and Automation Letters (2025)."}, {"review_id": "ZNYQJgJXbn", "reviewer": "Reviewer_9Vrp", "summary": "This paper introduces a novel, end-to-end neuromorphic framework for Unmanned Aerial Vehicles (UAVs) designed to handle navigation and dynamic obstacle avoidance simultaneously. The system uses a single event camera and an IMU as its sensory inputs. The core of the method consists of two main components: a bio-inspired \"Event RF Model\" for fast, low-latency detection and avoidance of moving obstacles, and a visual-homing algorithm that employs a Spiking Convolutional Neural Network (SCNN) to correct for IMU drift over long trajectories. To support the navigation component, the authors have created a new public dataset with over 50,000 event stream pairs for monocular pose correction. The authors validate their system in simulation and with real-world flight experiments, reporting very low latency (2.3 ms) and high energy efficiency.", "questions": "1. Dataset Generalizability: The pose correction dataset was collected with a gimbal-stabilized camera to simulate a UAV's stable attitude. How do you expect the trained network to perform with data from a real flight, which may contain more high-frequency vibrations and motion blur, even with a flight controller?\n2. Parameter Tuning: The Event RF Model has several key parameters ($\\Delta t, \\tau_i$, etc.). The paper provides a detailed analysis of how the final values were chosen. How sensitive is the model's performance to these parameters, and would they need to be re-tuned for different environments (e.g., outdoors vs. indoors) or different types of dynamic obstacles?\n3. Limits of the Potential Field: The obstacle avoidance relies on a 2D potential field generated from the camera's view. In scenarios with multiple obstacles at different depths, the 2D representation might merge them into a single repulsive area. What are the practical limits of this approach in cluttered scenes, and how does the system cope?", "limitations": "The authors rightly identify the reliance on IMU-based navigation and the lack of depth information from the monocular camera as key limitations. I would add that the dependency on a pre-flown \"outbound\" route is the most significant constraint on the system's autonomy, effectively limiting it to \"patrol\" or \"return-to-home\" style missions in known spaces.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "**Strengths:**\n- Promising Performance Metrics: The paper demonstrates impressive results in latency and power consumption, reporting an obstacle avoidance latency of just 2.3 ms and a total system energy consumption that is a fraction of conventional systems. These are strong results for onboard processing.\n- Novel Bio-Inspired Model: The Event RF Model, which draws inspiration from the frog's visual system, is an interesting and creative approach to dynamic object detection. By avoiding explicit object recognition and trajectory planning, it smartly leverages the properties of event data for fast reactions.\n- Dataset Contribution: The new event-based dataset for pose correction is a valuable contribution to the research community. The lack of such public datasets has been a barrier to entry, and this resource could enable more research in neuromorphic navigation.\n- Complete System Demonstration: The authors successfully integrate the perception and control modules into a complete system and demonstrate its functionality in real-world experiments on a quadrotor, which is a commendable engineering achievement.\n\n**Weaknesses:**\n\n- Significant Operational Constraints: The system's effectiveness is demonstrated under very specific conditions. The visual-homing navigation requires a pre-recorded outbound flight through a static environment, and the system assumes no dynamic obstacles are present during the recalibration maneuvers. These assumptions limit the system's utility for missions in unknown or dynamically changing environments.\n- Hardware Dependency: The impressive energy and latency results are tightly coupled with the use of the specialized Speck neuromorphic chip. While a great showcase for this hardware, it makes the results less generalizable and harder to reproduce for researchers without access to such specific processors. The performance gap between the SNN on Speck vs. a Jetson Orin highlights this dependency.\n- Limited Navigation Scheme: The reliance on visual-homing, while computationally efficient, is a significant limitation on autonomy. The drone can only navigate back along a known path, not explore or dynamically re-plan a route in an unfamiliar space. The paper acknowledges this, but it remains a primary weakness for a \"fully autonomous\" system.", "was_revised": true, "final_justification": "Author has addressed my concerns", "rebuttal": "Thank you for the insightful feedback and suggestions and we sincerely appreciate your positive assessment of our work—recognizing the \"*impressive results*\" achieved by the Event-RF Model, describing our approach as an \"*novel*\" and \"*interesting and creative solution*\", and acknowledging the system implementation as a \"*commendable engineering achievement*\".\n\n>**Q1: Dataset Generalizability: The pose correction dataset was collected with a gimbal-stabilized camera to simulate a UAV's stable attitude. How do you expect the trained network to perform with data from a real flight, which may contain more high-frequency vibrations and motion blur, even with a flight controller?**\n\nAfter training, we directly transferred the model to simulated environments for validation (Section 4.1; Supplementary Material 7). Given that the simulated quadrotor inherently exhibits high-frequency vibrations, we anticipated minimal performance degradation during real-world flight missions. This expectation was subsequently confirmed by our real-world test results (Section 4.2).\n\nRegarding motion blur: unlike frame-based RGB cameras, event cameras do not rely on focus-based imaging and thus avoid motion blur for high-speed objects. The only potential blur arises from accumulating events over excessively long time intervals. During both training and testing, we used a fixed time interval of *t* = 50 ms for event streams, which effectively mitigates this blur (Line 175: *\"The feature extractor... extracts features from two continuous event streams with a temporal time window of 50 ms\"*).\n\nWhile differences exist between event streams captured by a real quadrotor and those in our dataset, we conducted additional validation experiments. We captured 500 calibration event stream pairs using a quadrotor across 25 distinct preset scenarios and tested them directly with our trained network. Quantitative comparison between these quadrotor-captured results and the dataset results (shown below) further supports our claims.\n||Metric|X (m)|Y (m)|Yaw($\\degree$)|\n|-|-|-|-|-|\n|Dataset Event Streams|RMSE|0.062|0.058|2.3|\n||MAE|0.049|0.046|1.7|\n|Outdoor Event Streams|RMSE|0.068|0.066|2.5|\n||MAE|0.051|0.055|1.8|\n\nWe will include this experiment in Section 4.2 of the camera-ready version.\n\n>**Q2: Parameter Tuning: The Event RF Model has several key parameters ($\\Delta t$, $\\tau_i$, etc.). The paper provides a detailed analysis of how the final values were chosen. How sensitive is the model's performance to these parameters, and would they need to be re-tuned for different environments (e.g., outdoors vs. indoors) or different types of dynamic obstacles?**\n\nThe model showed parameter-dependent performance variations: while fine-tuned parameters worked robustly in most scenarios, targeted tuning enhanced extreme-scenario performance. We quantitatively evaluated this through controlled evasion tests where a stationary quadrotor avoided 30-cm-diameter balls (traveling at 5m/s) with different parameter sets (10 trials each). Additional validation used low- (2 m/s), high- (8 m/s), and ultra-high-speed (15 m/s) projectiles, confirming both general robustness and optimized-case improvements (see results below).\n|Type|Value|SR|Reason|\n|-|-|-|-|\n|$A_1$/$A_2$|0.5| \\ |Lost control|\n||0.75| \\ |Lost control|\n||1|100%||\n||1.22|100%||\n||1.5|100%||\n||2|90%||\n|$\\tau_e$, $\\tau_i$|~1| \\ |The energy decays too rapidly to suppress or enhance events|\n||~0.1|100%||\n||~0.01|50%|The energy decays too slowly, resulting in the suppression of all global events|\n|$I_ {th}$/$E_{th}$|1|100%|The quadrotor makes extra movements|\n||1.2|100%||\n||2|40%|IRF energy too high, resulting in the suppression of all global events|\n|$\\Delta t$|0.01|70%|The quadroto can only avoid objects with long trajectory projections within the camera's field of view|\n||0.05|100%||\n||0.1| \\ |The IRF cannot suppress the energy of the ERF promptly|\n\n|Type|SR|\n|-|-|\n|Low-speed|100%|\n|High-speed|100%|\n|Ultra-high-speed|70%|\n|Ultra-high-speed (specified parameters)|90%|\n\nWe will add the result analysis of this additional experiment in the main text and the detailed table in the supplementary material in the camera-ready version.\n\n>**Q3: Limits of the Potential Field: The obstacle avoidance relies on a 2D potential field generated from the camera's view. In scenarios with multiple obstacles at different depths, the 2D representation might merge them into a single repulsive area. What are the practical limits of this approach in cluttered scenes, and how does the system cope?**\n\nWe fully acknowledge this issue. Since our visual setup employs only a single monocular event camera, we cannot acquire depth information for individual event points, consequently making it impossible to determine the depth of potential fields. Our study primarily focuses on exploring a fully neuromorphic architecture for extremely lightweight quadrotor autonomous navigation and obstacle avoidance, which led us to strictly limit the number of sensors in our system.\n\nIn practical applications, if a stereo event camera system were adopted, the Event-RF Model could estimate depth by using parity between event points/streams generated by moving objects. This would allow the removal of event streams from distant moving objects, thereby preventing interference between potential fields from far and near objects. We conducted additional experiments to validate this approach, and we will further explore the stereo visual setup in future work.\n|Distance|Mean|Median|Stddev|M.A.D|\n|-|-|-|-|-|\n|<1m|1.6|1.3|0.7|0.6|\n|1-5m|5.5|4.5|1.8|1.3|\n|5-10m|14.5|12.7|4.5|3.5|\n|10-20m|34.2|27.1|11.4|8.7|\n\nIn the current work, constrained by our single-event-camera premise, we distinguish objects based on potential field size—distant objects naturally occupy smaller projections in the camera's field of view. During the conversion from raw PF to diffused PF, we employ the Two-Pass Algorithm (Line 223: *\"To solve this, we use the Two-Pass Algorithm...\"*) to eliminate excessively small high-potential regions.\n\nThis method does have limitations: when multiple dynamic objects are present, small potential fields may temporarily merge into larger ones. However, due to object motion, such merged regions persist only briefly before decomposing back into smaller, removable fields. While this may cause minor drone jitter during flight, it does not compromise overall performance.\n\nWe conducted additional experiments to validate the impact of background dynamic objects on quadrotor performance through five test scenarios: no objects, mid-range small (thrown balls), mid-range large (pedestrians), far-range small, and far-range large - each tested with 40 obstacle throws (tennis-sized, 1.5m distance). Close-range dynamics were excluded as proximity triggers for valid obstacle classification. Results showed distant objects and mid-range small objects caused negligible performance impacts. However, mid-range large objects slightly reduced avoidance success rates due to their persistent visual presence in the quadrotor's field of view.\n|Scenario|SR|\n|-|-|\n|No background objects|100%|\n|Mid-range small objects|95%|\n|Mid-range large objects|77.5%|\n|Far-range small objects|100%|\n|Far-range large objects|97.5%|\n\nWe will include these additional experiments in the camera-ready version.\n\n>**Regarding weaknesses 1 (Significant Operational Constraints) and 3 (Limited Navigation Scheme):** \n\nThe core objective of this work is to investigate the advantages of neuromorphic navigation and obstacle avoidance in tiny quadrotors operating in GPS-denied environments. Consequently, we imposed strict system constraints: using only a monocular event camera and IMU as sensors, with all computations (except essential processes) performed on neuromorphic hardware.\n\nIn practical applications, our method can be modularly integrated with most existing navigation and exploration algorithms. To validate this concept, we integrated the Event-RF Model with multiple navigation/exploration algorithms and tested them in completely unknown dynamic environments (results shown below), each with 20 trials and 140 dynamic obstacles in total. The observed mission success rate reflects our preliminary integration of Event-RF (either as an inserted module or dynamic obstacle replacement) without full system optimization. We anticipate substantial performance gains through dedicated adjustments and will include this experiment in the camera-ready version.\n|Method|Dynamic obstacle avoidance SR|\n|-|-|\n|EGO-Planner [1]|86.4%|\n|MAVRL [2]|90.0%|\n|NAVRL [3]|94.3%|\n\n>**Regarding weakness 2 (Hardware Dependency):**\n\nWe appreciate the reviewer's perspective regarding the current adoption challenges of neuromorphic computing. Indeed, the field faces an adoption paradox: the lack of widespread neuromorphic hardware has limited researchers' ability to demonstrate its advantages, which in turn slows hardware development - creating a vicious cycle. By demonstrating the advantages of neuromorphic architecture in extreme scenarios (energy consumption, latency, and computational resources), we hope to make these advantages known to more researchers and thus create a positive feedback loop, accelerate cost-effective hardware proliferation, and foster the development of the community. In ordinary scenarios, our system remains fully compatible with conventional hardware and still delivers promising real-time performance (12 ms vs ~20 ms)\n\nReferences\n\n[1] Xin Zhou, Zhepei Wang, Hongkai Ye, Chao Xu, and Fei Gao.  Ego-planner: An esdf-free gradient-based local planner for quadrotors. IEEE  Robotics  and  Automation  Letters, 6(2):478– 485, 2020\n\n[2] Yu, Hang, Christophe De Wagter, and Guido CH E. de Croon. \"MAVRL: Learn to fly in cluttered environments with varying speed.\" IEEE Robotics and Automation Letters (2024).\n\n[3] Xu, Zhefan, et al. \"Navrl: Learning safe flight in dynamic environments.\" IEEE Robotics and Automation Letters (2025)."}, {"review_id": "B4eNWzgUQ7", "reviewer": "Reviewer_Beb7", "summary": "This paper introduces a fully autonomous neuromorphic system for UAV navigation and dynamic obstacle avoidance, inspired by biological vision. The authors present a lightweight quadrotor setup that relies solely on a monocular event camera and an IMU, eliminating the need for external computation or infrastructure. The system leverages a spiking neural network (SCNN) and a visual-homing technique for navigation drift correction, and introduces a bio-inspired Event Receptive Field (Event RF) model, mimicking frog retinal neurons, to detect and dodge dynamic obstacles without identifying or tracking them. Impressively, the method achieves only 2.3 ms latency for obstacle evasion and maintains robust performance in varied lighting. The authors also contribute the first monocular event-based pose correction dataset with over 50,000 labeled samples. Overall, the work showcases a low-power, real-time neuromorphic pipeline tailored for small drones, offering a promising path forward for vision-driven autonomy in resource-constrained environments.", "questions": "1. Broader Generalization Beyond Indoor Scenarios:\nYour dataset and real-world validation are limited to indoor and relatively controlled environments. How does your model handle more diverse or cluttered settings, such as outdoors or highly dynamic crowds?\n\n2. Clarification on Parameter Sensitivity of the Event RF Model:\nYou explain the derivation of optimal parameters for the Event RF model, but much of this is relegated to the supplementary material. How sensitive is your system to these parameter choices in real-world deployments?\nSuggestion: A brief ablation study in the main paper (or summary thereof) could help clarify if the biologically inspired settings are crucial, or if other combinations would yield comparable performance. This would reinforce the scientific grounding behind the bio-inspiration claim.\n\n3. Hardware Availability and Software Adaptability:\nThe system is built around the Speck neuromorphic chip. For broader adoption, can your approach be ported to more available neuromorphic platforms (e.g., Loihi 2)?\nSuggestion: A discussion on portability and hardware-agnostic components would be very helpful. If possible, outlining which parts are tightly coupled to Speck and which can generalize would benefit practitioners.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Quality: The technical execution is highly impressive. The integration of a bio-inspired Event RF model with a neuromorphic hardware platform is well-engineered and rigorously evaluated. Latency reduction to 2.3 ms is no small feat, especially on a constrained platform like Speck. The experiments, both in simulation and real-world settings are comprehensive, and the success rates under diverse scenarios (e.g., lighting, obstacle speeds) inspire confidence in the robustness of the pipeline.\n\nClarity: Overall, the paper is clearly written and structured. The authors explain the biological inspiration behind their algorithm in a way that’s accessible yet grounded. I especially appreciate the inclusion of visual diagrams like the schematic on page 4 and the visual-homing and CalibNet workflow on page 6, they help demystify the system flow.\n\nSignificance: This work addresses a critical bottleneck in UAV autonomy: reliable operation without external infrastructure or high power budgets. The potential impact is significant, particularly for tiny drones navigating dynamic or GPS-denied environments. The contribution of a novel dataset is also valuable and likely to catalyze more research in event-based pose estimation. Though the energy reduction is a big win, the system still depends on relatively novel hardware (e.g., Speck), which may not yet be widely available. Broader adoption might hinge on more accessible neuromorphic chips or adaptable software implementations.\n\nOriginality: The fusion of biologically-inspired vision (modeled on frog retinal neurons) with a fully neuromorphic UAV control pipeline is quite original. While the frog-eye model is cleverly applied, there’s limited ablation on whether the biological modeling choices (e.g., receptive field asymmetry) are crucial or just one of many viable heuristics for event filtering. Some deeper analysis or comparison here would strengthen the originality claim further.", "was_revised": true, "final_justification": "I am maintaining my original score, which was already at the highest end in regards to the quality of the paper/work. The authors have provided clear and satisfactory responses during the rebuttal phase. While the questions raised were important, they served more as confirmation and follow-up to ensure the scientific rigor of the work, rather than indicating any serious concerns. The authors' replies demonstrated a strong understanding of the issues and confirmed that they are approaching the problem in a sound and principled manner. As such, I see no unresolved issues, and I continue to support acceptance of this submission.", "rebuttal": "Thank you for the insightful feedback and suggestions and we sincerely appreciate your recognition of our work's technical execution as \"*highly impressive*\" and the assessment that our dataset is \"*valuable and likely to catalyze more research.*\" We are particularly grateful for your acknowledgment of our bio-inspired model as \"*quite original*\" and \"*offering a promising path forward*\".\n\n>**Q1: Broader Generalization Beyond Indoor Scenarios: Your dataset and real-world validation are limited to indoor and relatively controlled environments. How does your model handle more diverse or cluttered settings, such as outdoors or highly dynamic crowds?**\n\nUnlike traditional RGB cameras, event cameras record brightness changes rather than static images, yielding higher dynamic range and illumination invariance (Section 4.2). Thus, while our dataset was collected indoors, event data characteristics should remain valid outdoors. The sole limitation stems from excessive temporal accumulation in event streams, which we address by using 50 ms intervals (Line 175: *\"The feature extractor of the network extracts features from two continuous event streams with a temporal time window of 50 ms.\"*) to minimize motion blur. This configuration has proven effective in both indoor and outdoor real-world applications through experimental validation, as shown below:\n||Metric|X (m)|Y (m)|Yaw ($\\degree$)|\n|-|-|-|-|-|\n|Dataset Event Streams|RMSE|0.062|0.058|2.3|\n||MAE|0.049|0.046|1.7|\n|Outdoor Event Streams|RMSE|0.068|0.066|2.5|\n||MAE|0.051|0.055|1.8|\n\nWe conducted additional experiments in both outdoor (a square with static boxes) and cluttered indoor environments (office corridors), testing three dynamic obstacles: thrown objects, sparse crowds, and dense crowds. Results show consistent performance across environments, with outdoor lighting/airflow variations causing no significant impact. The system maintained high navigation success rates for thrown objects and sparse crowds, with performance degrading only in extreme crowd densities.\n|Scenario|Obstacle type|SR|\n|-|-|-|\n|Office building corridor|Thrown objects|94.6%|\n||Sparse pedestrians|93.8%|\n||Dense pedestrians|62.1%|\n|Outdoor square|Thrown objects|94.1%|\n||Sparse pedestrians|93.7%|\n||Dense pedestrians|54.8%|\n\nThis limitation primarily stems from our strict sensor constraints: with only a monocular event camera, depth estimation becomes unavailable, preventing distance assessment for obstacles. Our current solution employs the Two-Pass Algorithm (Line 223: *\"To solve this, we use the Two-Pass Algorithm to make a connected component detection\"*) to evaluate obstacle threat levels through 2D projections of event-converted potential fields. However, excessive pedestrian density causes overlapping projections that merge multiple potential fields, thereby compromising algorithm effectiveness.\n\nWe posit that adopting stereo event cameras in future implementations could resolve this issue by transforming 2D potential fields into 3D representations using depth information. Supplementary experiments have confirmed the feasibility of depth estimation from filtered event streams using such stereo configurations.\n|Distance|Mean|Median|Stddev|M.A.D|\n|-|-|-|-|-|\n|<1m|1.6|1.3|0.7|0.6|\n|1-5m|5.5|4.5|1.8|1.3|\n|5-10m|14.5|12.7|4.5|3.5|\n|10-20m|34.2|27.1|11.4|8.7|\n\nWe will include these additional experiments in the camera-ready version.\n\n>**Q2: Clarification on Parameter Sensitivity of the Event RF Model: You explain the derivation of optimal parameters for the Event RF model, but much of this is relegated to the supplementary material. How sensitive is your system to these parameter choices in real-world deployments? Suggestion: A brief ablation study in the main paper (or summary thereof) could help clarify if the biologically inspired settings are crucial, or if other combinations would yield comparable performance. This would reinforce the scientific grounding behind the bio-inspiration claim.**\n\nBased on the bio-inspired properties of the Event-RF model, we determined the optimal parameter values through mathematical analysis and the study of actual performance characteristics of frog retinal ganglion cells and their receptive fields. To further investigate the model's sensitivity to each parameter and its adaptability across different scenarios, we conducted additional experiments to quantitatively analyze the Event-RF model's sensitivity and performance. We quantitatively evaluated this through controlled evasion tests where a stationary quadrotor avoided 30-cm-diameter balls (traveling at 5m/s) with different parameter sets (10 trials each). Additional validation used low- (2m/s), high- (8m/s), and ultra-high-speed (15m/s) projectiles, confirming both general robustness and optimized-case improvements (see results below). We will add the results of the analysis to Section 4.3 in the camera-ready version.\n\n|Type|Value|SR|Reason|\n|-|-|-|-|\n|$A_1$/$A_2$|0.5| \\ |Lost control|\n||0.75| \\ |Lost control|\n||1|100%||\n||1.22|100%||\n||1.5|100%||\n||2|90%||\n|$\\tau_e$, $\\tau_i$|~1| \\ |The energy decays too rapidly to suppress or enhance events|\n||~0.1|100%||\n||~0.01|50%|The energy decays too slowly, resulting in the suppression of all global events|\n|$I_ {th}$/$E_{th}$|1|100%|The quadrotor makes extra movements|\n||1.2|100%||\n||2|40%|IRF energy too high, resulting in the suppression of all global events|\n| $\\Delta t$ |0.01|70%|The quadroto can only avoid objects with long trajectory projections within the camera's field of view|\n||0.05|100%||\n||0.1| \\ |The IRF cannot suppress the energy of the ERF promptly|\n\n|Type|SR|\n|-|-|\n|Low-speed|100%|\n|High-speed|100%|\n|Ultra-high-speed|70%|\n|Ultra-high-speed (specified parameters)|90%|\n\n>**Q3: Hardware Availability and Software Adaptability: The system is built around the Speck neuromorphic chip. For broader adoption, can your approach be ported to more available neuromorphic platforms (e.g., Loihi 2)? Suggestion: A discussion on portability and hardware-agnostic components would be very helpful. If possible, outlining which parts are tightly coupled to Speck and which can generalize would benefit practitioners.**\n\nWe deployed our system on Speck primarily because neuromorphic algorithms (SNNs, event-based) achieve optimal performance on neuromorphic hardware through spike-based computation. However, our algorithm requires no Speck-specific features - as shown by successful deployment on conventional hardware (Jetson Orin NX) despite increased power consumption (4.64 W→22.4 W) and latency (2.3 ms→12.5 ms). （Supplementary Note 10)\n\nOnly the Energy Consumption Monitor uses platform-specific features for power measurement. While unavailable for testing on other neuromorphic chips (e.g., Loihi), our architecture avoids hardware-specific dependencies. Shared neuromorphic computing principles and proven ANN compatibility suggest minimal porting barriers.\n\nUpon publication, we will open-source both ANN-compatible and Speck-optimized implementations, followed by migration guides for other neuromorphic platforms as access becomes available.\n\n>**Regarding \"there's limited ablation on whether the biological modeling choices (e.g., receptive field asymmetry) are crucial or just one of many viable heuristics for event filtering. Some deeper analysis or comparison here would strengthen the originality claim further.\"**\n\nThe biological modeling approach was adopted because its effectiveness has been evolutionarily validated in nature, providing robust performance. Our primary focus is not to investigate the filtering method itself, but rather to leverage this bio-inspired approach to demonstrate enhanced navigation and obstacle avoidance capabilities within a neuromorphic framework. We intentionally selected this proven biological strategy rather than optimizing the filtering methodology, as this work aims to validate neuromorphic principles rather than compare filtering techniques.\n\nWe agree with reviewer Beb7 that deeper analysis and comparison with different filtering approaches could significantly increase the work's contribution, and could also potentially illustrate the strength of bio-inspired approaches."}, {"review_id": "3RKtzjaJZA", "reviewer": "Reviewer_ZD1j", "summary": "This paper presents a neuromorphic framework to enabling real-time navigation and dynamic obstable avoidance without reliance on external infrastructure or traditional vision systems. The proposed work draws inpiration from biological vision systems, which enables accurate moving object detection and avoidance with low latency and compute.\n\nThe key contributions of this work are\n1. A fully neuromorphic framework for dynamic obstacle avoidance\n2. A bio-inspired dynamic obstacle avoidance  event RF model\n3. A (first) dataset for monocular event-based pose correction dataset", "questions": "N/A", "limitations": "In \"Broader Impacts\" (Q10), the paper says \"There is no societal impact of the work performed\". I beg to disagree. It is well-known that UAVs, especially ones with enhanced obstacle avoidance capabilities, can have significant societal impacts, both positive and negative. Postive impacts include search and rescue, inspection and monitoring. Negative impacts include military applications, privacy infringment, etc. The authors simply claiming that there is \"no societal impact\" maybe seen as an oversight, especially for researching working on UAVs.  \n\nIn \"Safeguards\" (Q11), the paper anserws \"The paper poses no such risks\". Although the question only lists potential misues like LLM, image generators, scraped datasets, the current research for highly autonomous and agile drones could ptentially be misuesd in military applications. A discussion of safeguards, even if not includedin this work, is a demonstration of ethical consideration.", "rating": 5, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## Strength\n\nThe work presented directly addresses a big challenge in UAV navigation, which is the reliance on either external sensors like GPS, or traditional sensors like camera/lidar. In particular, the paper demnstrates a breakthrough in latency and energy efficiency for obstacle avoidance for UAVs. The 2.3 ms latency for end-to-en obstacle avoidance is quite low, which provides a large reaction time window for high-speed ydnamic obstacles for up to 10 m/s. This low latency outperforms prior arts that rely on traditional visioni sensors by an order of magnitude and also outperforms previous event-based approach by a lot, while being energy efficient. \n\nThe event RF model is a novel approach that is directly inspired by the visual neurons of frogs. It leverages the ERF-IRF spatial asymmetry to suppress static events and amplify dynamic ones, thus bypassing computationally expensive steps like target recognition and trajectory computation. \n\n**Clarity**\n\nThe paper is clearly-written and well-structured. I did not find any major issues with mathematical formulations, figures and tables. There are some small improvements that the authors could make, such as: \n1. reducing the number of decimals in table 1, or even better, converting it to a figure and move the table to appendix. \n2. Adding a trajectory curve in figure 4.\n\n**Dataset**\n\nThe creation of a monocular event-based pose correction dataset could also be useful  for advancing research in event-based/bio-inspired navigation.\n\n**Experiments**\n\nThe papers claim is well supported by experimental validation and analysis. The experiments include both simulation and real-world ones. The paper also includes comparison with SOTA approaches and shows significant improvments.\n\n## Weakness\n\nThere are a few limitations stated by the authors, include: \n1. the current work still relies on IMU for navigation. \n2. lack of scale/depth estimation from monocular sensor. \n\nBeyond the above limitations explicitly acknowledged by the authors, there are several other weaknesses:\n\nthe real-world experiments were conducted in a relatively small indoor area, without any disturbances like sudden wind changes, and/or other background moving obstacles. It seems like all experiments conducted focus on single dynamic obstacles, therefore, the impact of additional background dynamic objects on the two-pass algorithm is not fully validated. \n\nAdditionaly, it is not clear how the UAV would navigate in a cluttered environment with static obstacles only. Since the main objective of the paper is on dynamic obstacle avoidance, this is fine, but since the potential field is only focused on dynamic objects, the UAV will have no way to avoid static obstacles. I'd appreciate the authors shed some light on this.\n\nAnother aspect not fully explored is more complicated dynamic obstacle behavior. The experiments only cover \"thrown\" obstacles with various sizes. These obstacles have pretty much pre-determined trajectory and is thus fairly easy to avoid. It would be interested to see how the algorithm could cope with obstacles that undergo complex or even adversaial motion. But I can see this line of research being done in future work.", "was_revised": true, "final_justification": "After reading the authors' rebuttal, I believe they have addressed most of my concerns in the initial review, specifically on dynamic obstacles. Echoing the ethics review, I've increased my score from 4 to 5, under the condition that the authors would make revision that addresses ethics and safety concerns.", "rebuttal": "Thank you for the insightful feedback and suggestions and we are deeply grateful for your recognition of our work's latency and energy efficiency as a *\"breakthrough\"*, and we are honored by your characterization of Event-RF's robustness as a *\"novel approach.\"* Your acknowledgment that our dataset is *\"useful for advancing research\"* further validates its value to the neuromorphic community.\n\n>**Regarding Clarity: \"reducing the number of decimals in table 1, or even better, converting it to a figure and move the table to appendix.\" and \"Adding a trajectory curve in figure 4.\"**\n\nWe have already converted the table to a figure and added a trajectory in Figure 4. We will add these figures in the camera-ready version.\n\n>**Regarding \"the real-world experiments were conducted in a relatively small indoor area, without any disturbances like sudden wind changes, and/or other background moving obstacles. It seems like all experiments conducted focus on single dynamic obstacles, therefore, the impact of additional background dynamic objects on the two-pass algorithm is not fully validated.\"**\n\nEvent cameras inherently avoid motion blur in outdoor airflow disturbances by detecting brightness changes instead of relying on focus-based imaging. Potential blur from event accumulation is minimized by our 50 ms time window setting for event stream processing (Line 175: *\"The feature extractor of the network extracts features from two continuous event streams with a temporal time window of 50 ms\"*). We validated this through quadrotor outdoor tests under varying wind conditions, demonstrating robust navigation performance.\n||Metric|X (m)|Y (m)|Yaw ($\\degree$)|\n|-|-|-|-|-|\n|Dataset Event Streams|RMSE|0.062|0.058|2.3|\n||MAE|0.049|0.046|1.7|\n|Outdoor Event Streams|RMSE|0.068|0.066|2.5|\n||MAE|0.051|0.055|1.8|\n\nRegarding the Two-Pass algorithm, we argue that dynamic background objects would not significantly impact its performance.\n\n1. Mid-range moving objects (generating Two-Pass-resistant potential fields) can be eliminated through quadrotor maneuvers: the event camera's narrow field of view converts minor evasive actions into significant view displacement, clearing both the object and its potential field from the visual frame.\n\n2. Distant objects pose no interference as their potential fields are systematically removed by the Two-Pass algorithm.\n\nAdditional experiments validated the impact of background dynamic objects on drone performance through five test scenarios: no objects, mid-range small (thrown balls), mid-range large (pedestrians), far-range small, and far-range large - each tested with 40 obstacle throws (tennis-sized, 1.5m distance). Close-range dynamics were excluded as proximity triggers valid obstacle classification. Results showed distant objects and mid-range small objects caused negligible performance impacts. However, mid-range large objects slightly reduced avoidance success rates due to their persistent visual presence in the quadrotor's field of view.\n|Scenario|SR|\n|-|-|\n|No background objects|100%|\n|Mid-range small objects|95%|\n|Mid-range large objects|77.5%|\n|Far-range small objects|100%|\n|Far-range large objects|97.5%|\n\nPerformance degradation arises from monocular event cameras' inability to perceive depth, causing artificial potential fields to encode only potential energy without depth information. This results in merged potential fields for mid-range large objects. A stereo vision setup could resolve this limitation in future implementations by adding depth information to potential fields. We validated the binocular architecture's effectiveness through additional experiments where the stereo system estimated the depth of event streams and calculated the difference between the estimation and ground truth (GT):\n|Distance|Mean|Median|Stddev|M.A.D|\n|-|-|-|-|-|\n|<1m|1.6|1.3|0.7|0.6|\n|1-5m|5.5|4.5|1.8|1.3|\n|5-10m|14.5|12.7|4.5|3.5|\n|10-20m|34.2|27.1|11.4|8.7|\n\nWe will add these additional experiments in the camera-ready version.\n\n>**Regarding \"it is not clear how the UAV would navigate in a cluttered environment with static obstacles only. Since the main objective of the paper is on dynamic obstacle avoidance, this is fine, but since the potential field is only focused on dynamic objects, the UAV will have no way to avoid static obstacles. I'd appreciate the authors shed some light on this.\"**\n\nThis work focuses on bio-inspired neuromorphic systems for autonomous navigation in GPS-denied environments under strict hardware constraints: only a monocular event camera and IMU, with near-full neuromorphic computation. For static obstacle avoidance, the quadrotor first records an obstacle-free path during outbound flight, then precisely retraces it using recorded IMU data with visual correction, hence inherently avoiding all static obstacles along the flight path (as the outbound route was already obstacle-free)\n\nHowever, for flight in unknown environments and exploration tasks, our method can be modularly integrated with most existing navigation and exploration algorithms in practical applications. By deploying the navigation algorithm on the quadrotor's onboard computer while running our obstacle avoidance module on neuromorphic chips, the system achieves simultaneous navigation and highly efficient obstacle avoidance. To validate this concept, we integrated the Event-RF Model with multiple navigation/exploration algorithms and tested them in completely unfamiliar dynamic environments (results shown below).\n\nFor unknown environment exploration, our approach enables modular integration with existing navigation systems. By deploying standard navigation algorithms on the onboard computer while executing neuromorphic obstacle avoidance, we achieve simultaneous navigation and collision prevention.  To validate this concept, we integrated the Event-RF Model with multiple navigation/exploration algorithms and tested them in completely unknown dynamic environments (results shown below), each with 20 trials and 140 dynamic obstacles in total. The observed mission success rate reflects our preliminary integration of Event-RF (either as an inserted module or dynamic obstacle replacement) without full system optimization. We anticipate substantial performance gains through dedicated adjustments. We will include this additional experiment in the supplementary material of the camera-ready version.\n|Method|Dynamic obstacle avoidance SR|\n|-|-|\n|EGO-Planner [1]|86.4%|\n|MAVRL [2]|90.0%|\n|NAVRL [3]|94.3%|\n\n>**Regarding \"The experiments only cover \"thrown\" obstacles with various sizes. These obstacles have pretty much pre-determined trajectory and is thus fairly easy to avoid. It would be interested to see how the algorithm could cope with obstacles that undergo complex or even adversaial motion.\"**\n\nRegarding the avoidance of self-propelled dynamic obstacles (e.g., other quadrotors), this scenario involves predicting the trajectory variations of such moving obstacles. While our current method adopts a reactive avoidance approach that does not compute trajectories, we acknowledge that more sophisticated decision-making modules would be required to handle complex trajectory changes from other flying objects. We plan to address this challenge in our future work. For low-speed dynamic obstacles that exhibit complex motion (pedestrians), we conducted an additional experiment to test the system's performance. Experimental results demonstrate that our system can effectively handle such low-speed dynamic obstacles with trajectory variations, as its reactive avoidance capability already exceeds the maximum velocity threshold of low-speed moving obstacles.\n|Scenario|Obstacle type|SR|\n|-|-|-|\n|Office building corridor|Thrown objects|94.6%|\n||Sparse pedestrians|93.8%|\n|Outdoor square|Thrown objects|94.1%|\n||Sparse pedestrians|93.7%|\n\nWe will include this experiment in Section 4.2 of the camera-ready version.\n\n>**Regarding Broader Impacts and Safeguards**\n\nWe resonate with the reviewer's concern regarding both broader impact and safeguards. The development of quadrotor technology can play a positive role in multiple fields such as inspection, search and rescue, and transportation. Particularly, autonomous navigation and obstacle avoidance technology that operates without external signals can effectively address challenges in environments with poor signal coverage, including urban areas, caves, and forests. However, it may also lead to negative consequences such as privacy violations. For potential privacy infringement, our algorithm employs event cameras as sensors, which by design only capture illumination changes rather than detailed biological information. This fundamental characteristic makes it physically impossible to obtain identifiable imagery, thereby inherently preventing privacy violations.\n\nWe have the following safeguards to mitigate potential negative broader impacts:\n\n1. Explicitly prohibit harmful applications or misuse (e.g., surveillance, privacy violations) in our open-source license\n\n2. Include clear usage restrictions in the code documentation\n\n3. Expand our discussion of societal impact and safeguards in the Conclusion section in the camera-ready version\n\nThese measures will formally articulate our position and demonstrate proactive efforts to prevent technology misuse, while maintaining the technology's availability for beneficial applications.\n\nReferences\n\n[1] Xin Zhou, Zhepei Wang, Hongkai Ye, Chao Xu, and Fei Gao.  Ego-planner: An esdf-free gradient-based local planner for quadrotors. IEEE  Robotics  and  Automation  Letters, 6(2):478– 485, 2020\n\n[2] Yu, Hang, Christophe De Wagter, and Guido CH E. de Croon. \"MAVRL: Learn to fly in cluttered environments with varying speed.\" IEEE Robotics and Automation Letters (2024).\n\n[3] Xu, Zhefan, et al. \"Navrl: Learning safe flight in dynamic environments.\" IEEE Robotics and Automation Letters (2025)."}]

Author rebuttal:

Meta-review: The authors introduce an event-based framework for navigation and dynamic obstacle avoidance. For navigation, they propose to use snapshot-based route-following, as recently published in van Dijk et al. in Science Robotics. Instead of single pictures, though, they use small event-streams for cancelling odometry drift. For the avoidance of dynamic objects, the authors use a frog-eye-inspired filtering scheme that highlights dynamic obstacles. A potential-based avoidance method is used. Some parts of the processing pipeline are more "traditional" (e.g., finding connected components), some are embedded in spiking neural networks, implemented on the Synsense Speck. 

The authors successfully addressed reviewer concerns on the generalization of the method to outdoor conditions, the limitations of the navigation method, and parameter sensitivity. They seem to have performed substantial additional experiments to address these concerns. It will require some adjustments to the main article and supplementary material, but in my eyes nothing that would require another review round.

All in all, this is in my eyes a clear accept.
Decision: Accept (spotlight)