OpenRaiser/Intern-Atlas huggingface dataset:
Occlusion Reasoning for Skeleton Extraction of Self-Occluded Tree Canopies
Venue: Conference ICRA 2023
Year: 2023

In this work, we present a method to extract the skeleton of a self-occluded tree canopy by estimating the unobserved structures of the tree. A tree skeleton compactly describes the topological structure and contains useful information such as branch geometry, positions and hierarchy. This can be critical to planning contact interactions for agricultural manipulation, yet is difficult to gain due to occlusion by leaves, fruits and other branches. Our method uses an instance segmentation network to detect visible trunk, branches, and twigs. Then, based on the observed tree structures, we build a custom 3D likelihood map in the form of an occupancy grid to hypothesize on the presence of occluded skeletons through a series of minimum cost path searches. We show that our method outperforms baseline methods in highly occluded scenes, demonstrated through a set of experiments on a synthetic tree dataset. Qualitative results are also presented on a real tree dataset collected from the field.
Robust Imaging Sonar-based Place Recognition and Localization in Underwater Environments
Venue: Conference ICRA 2023
Year: 2023

Place recognition using SOund Navigation and Ranging (SONAR) images is an important task for simultaneous localization and mapping(SLAM) in underwater environments. This paper proposes a robust and efficient imaging SONAR based place recognition, SONAR context, and loop closure method. Unlike previous methods, our approach encodes geometric information based on the characteristics of raw SONAR measurements without prior knowledge or training. We also design a hierarchical searching procedure for fast retrieval of candidate SONAR frames and apply adaptive shifting and padding to achieve robust matching on rotation and translation changes. In addition, we can derive the initial pose through adaptive shifting and apply it to the iterative closest point (ICP) based loop closure factor. We evaluate the performance of SONAR context in the various underwater sequences such as simulated open water, real water tank, and real underwater environments. The proposed approach shows the robustness and improvements of place recognition on various datasets and evaluation metrics. Supplementary materials are available at https://github.com/sparolab/sonar_context.git.
A Reachability Tree-Based Algorithm for Robot Task and Motion Planning
Venue: Conference ICRA 2023
Year: 2023

This paper presents a novel algorithm for robot task and motion planning (TAMP) problems by utilizing a reachability tree. While tree-based algorithms are known for their speed and simplicity in motion planning (MP), they are not well-suited for TAMP problems that involve both abstracted and geometrical state variables. To address this challenge, we propose a hierarchical sampling strategy, which first generates an abstracted task plan using Monte Carlo tree search (MCTS) and then fills in the details with a geometrically feasible motion trajectory. Moreover, we show that the performance of the proposed method can be significantly enhanced by selecting an appropriate reward for MCTS and by using a pre-generated goal state that is guaranteed to be geometrically feasible. A comparative study using TAMP benchmark problems demonstrates the effectiveness of the proposed approach.
WEDGE: Web-Image Assisted Domain Generalization for Semantic Segmentation
Venue: Conference ICRA 2023
Year: 2023

Domain generalization for semantic segmentation is highly demanded in real applications, where a trained model is expected to work well in previously unseen domains. One challenge lies in the lack of data which could cover the diverse distributions of the possible unseen domains for training. In this paper, we propose a WEb-image assisted Domain GEneralization (WEDGE) scheme, which is the first to exploit the diversity of web-crawled images for generalizable semantic segmentation. To explore and exploit the real-world data distributions, we collect web-crawled images which present large diversity in terms of weather conditions, sites, lighting, camera styles, etc. We also present a method which injects styles of the web-crawled images into training images on-the-fly during training, which enables the network to experience images of diverse styles with reliable labels for effective training. Moreover, we use the web-crawled images with their predicted pseudo labels for training to further enhance the capability of the network. Extensive experiments demonstrate that our method clearly outperforms existing domain generalization techniques.
EXOT: Exit-aware Object Tracker for Safe Robotic Manipulation of Moving Object
Venue: Conference ICRA 2023
Year: 2023

Current robotic hand manipulation narrowly operates with objects in predictable positions in limited environments. Thus, when the location of the target object deviates severely from the expected location, a robot sometimes responds in an unexpected way, especially when it operates with a human. For safe robot operation, we propose the EXit-aware Object Tracker (EXOT) on a robot hand camera that recognizes an object's absence during manipulation. The robot decides whether to proceed by examining the tracker's bounding box output containing the target object. We adopt an out-of-distribution classifier for more accurate object recognition since trackers can mistrack a background as a target object. To the best of our knowledge, our method is the first approach of applying an out-of-distribution classification technique to a tracker output. We evaluate our method on the first-person video benchmark dataset, TREK-150, and on the custom dataset, RMOT-223, that we collect from the UR5e robot. Then we test our tracker on the UR5e robot in real-time with a conveyor-belt sushi task, to examine the tracker's ability to track target dishes and to determine the exit status. Our tracker shows 38% higher exit-aware performance than a baseline method. The dataset and the code will be released at https://github.com/hskAlena/EXOT.
Wild-Places: A Large-Scale Dataset for Lidar Place Recognition in Unstructured Natural Environments
Venue: Conference ICRA 2023
Year: 2023

Many existing datasets for lidar place recognition are solely representative of structured urban environments, and have recently been saturated in performance by deep learning based approaches. Natural and unstructured environments present many additional challenges for the tasks of long-term localisation but these environments are not represented in currently available datasets. To address this we introduce Wild-Places, a challenging large-scale dataset for lidar place recognition in unstructured, natural environments. Wild-Places contains eight lidar sequences collected with a handheld sensor payload over the course of fourteen months, containing a total of 63K undistorted lidar submaps along with accurate 6DoF ground truth. Our dataset contains multiple revisits both within and between sequences, allowing for both intra-sequence (i.e. loop closure detection) and inter-sequence (i.e. re-localisation) place recognition. We also benchmark several state-of-the-art approaches to demonstrate the challenges that this dataset introduces, particularly the case of long-term place recognition due to natural environments changing over time. Our dataset and code will be available at https://csiro-robotics.github.io/Wild-Places.
Explainable Action Prediction through Self-Supervision on Scene Graphs
Venue: Conference ICRA 2023
Year: 2023

This work explores scene graphs as a distilled representation of high-level information for autonomous driving, applied to future driver-action prediction. Given the scarcity and strong imbalance of data samples, we propose a self-supervision pipeline to infer representative and well-separated embeddings. Key aspects are interpretability and explainability; as such, we embed in our architecture attention mechanisms that can create spatial and temporal heatmaps on the scene graphs. We evaluate our system on the ROAD dataset against a fully-supervised approach, showing the superiority of our training regime.
General, Single-shot, Target-less, and Automatic LiDAR-Camera Extrinsic Calibration Toolbox
Venue: Conference ICRA 2023
Year: 2023

This paper presents an open source LiDAR-camera calibration toolbox that is general to LiDAR and camera projection models, requires only one pairing of LiDAR and camera data without a calibration target, and is fully automatic. For automatic initial guess estimation, we employ the SuperGlue image matching pipeline to find 2D-3D correspondences between LiDAR and camera data and estimate the LiDAR-camera transformation via RANSAC. Given the initial guess, we refine the transformation estimate with direct LiDAR-camera registration based on the normalized information distance, a mutual information-based cross-modal distance metric. For a handy calibration process, we also present several assistance capabilities (e.g., dynamic LiDAR data integration and user interface for making 2D-3D correspondence manually). The experimental results show that the proposed toolbox enables calibration of any combination of spinning and non-repetitive scan LiDARs and pinhole and omnidirectional cameras, and shows better calibration accuracy and robustness than those of the state-of-the-art edge-alignment-based calibration method.
ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenation
Venue: Conference ICRA 2023
Year: 2023

Transferring knowledge learned from the labeled source domain to the raw target domain for unsupervised domain adaptation (UDA) is essential to the scalable deployment of autonomous driving systems. State-of-the-art methods in UDA often employ a key idea: utilizing joint supervision signals from both source and target domains for self-training. In this work, we improve and extend this aspect. We present ConDA, a concatenation-based domain adaptation framework for LiDAR segmentation that: 1) constructs an intermediate domain consisting of fine-grained interchange signals from both source and target domains without destabilizing the semantic coherency of objects and background around the ego-vehicle; and 2) utilizes the intermediate domain for self-training. To improve the network training on the source domain and self-training on the intermediate domain, we propose an anti-aliasing regularizer and an entropy aggregator to reduce the negative effect caused by the aliasing artifacts and noisy pseudo labels. Through extensive studies, we demonstrate that ConDA significantly outperforms prior arts in mitigating domain gaps.
Interactive Object Segmentation in 3D Point Clouds
Venue: Conference ICRA 2023
Year: 2023

We propose an interactive approach for 3D instance segmentation, where users can iteratively collaborate with a deep learning model to segment objects in a 3D point cloud directly. Current methods for 3D instance segmentation are generally trained in a fully-supervised fashion, which requires large amounts of costly training labels, and does not generalize well to classes unseen during training. Few works have attempted to obtain 3D segmentation masks using human interactions. Existing methods rely on user feedback in the 2D image domain. As a consequence, users are required to constantly switch between 2D images and 3D representations, and custom architectures are employed to combine multiple input modalities. Therefore, integration with existing standard 3D models is not straightforward. The core idea of this work is to enable users to interact directly with 3D point clouds by clicking on desired 3D objects of interest~(or their background) to interactively segment the scene in an open-world setting. Specifically, our method does not require training data from any target domain, and can adapt to new environments where no appropriate training sets are available. Our system continuously adjusts the object segmentation based on the user feedback and achieves accurate dense 3D segmentation masks with minimal human effort (few clicks per object). Besides its potential for efficient labeling of large-scale and varied 3D datasets, our approach, where the user directly interacts with the 3D environment, enables new applications in AR/VR and human-robot interaction.
Tackling Clutter in Radar Data - Label Generation and Detection Using PointNet++
Venue: Conference ICRA 2023
Year: 2023

Radar sensors employed for environment perception, e.g. in autonomous vehicles, output a lot of unwanted clutter. These points, for which no corresponding real objects exist, are a major source of errors in following processing steps like object detection or tracking. We therefore present two novel neural network setups for identifying clutter. The input data, network architectures and training configuration are adjusted specifically for this task. Special attention is paid to the downsampling of point clouds composed of multiple sensor scans. In an extensive evaluation, the new setups display substantially better performance than existing approaches. Because there is no suitable public data set in which clutter is annotated, we design a method to automatically generate the respective labels. By applying it to existing data with object annotations and releasing its code, we effectively create the first freely available radar clutter data set representing real-world driving scenarios. Code and instructions are accessible at www.github.com/kopp-j/clutter-ds.
Adaptive-SpikeNet: Event-based Optical Flow Estimation using Spiking Neural Networks with Learnable Neuronal Dynamics
Venue: Conference ICRA 2023
Year: 2023

Event-based cameras have recently shown great potential for high-speed motion estimation owing to their ability to capture temporally rich information asynchronously. Spiking Neural Networks (SNNs), with their neuro-inspired event-driven processing can efficiently handle such asynchronous data, while neuron models such as the leaky-integrate and fire (LIF) can keep track of the quintessential timing information contained in the inputs. SNNs achieve this by maintaining a dynamic state in the neuron memory, retaining important information while forgetting redundant data over time. Thus, we posit that SNNs would allow for better performance on sequential regression tasks compared to similarly sized Analog Neural Networks (ANNs). However, deep SNNs are difficult to train due to vanishing spikes at later layers. To that effect, we propose an adaptive fully-spiking framework with learnable neuronal dynamics to alleviate the spike vanishing problem. We utilize surrogate gradient-based backpropagation through time (BPTT) to train our deep SNNs from scratch. We validate our approach for the task of optical flow estimation on the Multi-Vehicle Stereo Event-Camera (MVSEC) dataset and the DSEC-Flow dataset. Our experiments on these datasets show an average reduction of 13% in average endpoint error (AEE) compared to state-of-the-art ANNs. We also explore several down-scaled models and observe that our SNN models consistently outperform similarly sized ANNs offering 10%-16% lower AEE. These results demonstrate the importance of SNNs for smaller models and their suitability at the edge. In terms of efficiency, our SNNs offer substantial savings in network parameters (48.3x) and computational energy (10.2x) while attaining ~10% lower EPE compared to the state-of-the-art ANN implementations.
Multi-Target Pursuit by a Decentralized Heterogeneous UAV Swarm using Deep Multi-Agent Reinforcement Learning
Venue: Conference ICRA 2023
Year: 2023

Multi-agent pursuit-evasion tasks involving intelligent targets are notoriously challenging coordination problems. In this paper, we investigate new ways to learn such coordinated behaviors of unmanned aerial vehicles (UAVs) aimed at keeping track of multiple evasive targets. Within a Multi-Agent Reinforcement Learning (MARL) framework, we specifically propose a variant of the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method. Our approach addresses multi-target pursuit-evasion scenarios within non-stationary and unknown environments with random obstacles. In addition, given the critical role played by collective exploration in terms of detecting possible targets, we implement heterogeneous roles for the pursuers for enhanced exploratory actions balanced by exploitation (i.e. tracking) of previously identified targets. Our proposed role-based MADDPG algorithm is not only able to track multiple targets, but also is able to explore for possible targets by means of the proposed Voronoi-based rewarding policy. We implemented, tested and validated our approach in a simulation environment prior to deploying a real-world multi-robot system comprising of Crazyflie drones. Our results demonstrate that a multi-agent pursuit team has the ability to learn highly efficient coordinated control policies in terms of target tracking and exploration even when confronted with multiple fast evasive targets in complex environments.
Vitreoretinal Surgical Robotic System with Autonomous Orbital Manipulation using Vector-Field Inequalities
Venue: Conference ICRA 2023
Year: 2023

Vitreoretinal surgery pertains to the treatment of delicate tissues on the fundus of the eye using thin instruments. Surgeons frequently rotate the eye during surgery, which is called orbital manipulation, to observe regions around the fundus without moving the patient. In this paper, we propose the autonomous orbital manipulation of the eye in robot-assisted vitreoretinal surgery with our tele-operated surgical system. In a simulation study, we preliminarily investigated the increase in the manipulability of our system using orbital manipulation. Furthermore, we demonstrated the feasibility of our method in experiments with a physical robot and a realistic eye model, showing an increase in the view-able area of the fundus when compared to a conventional technique. Source code and minimal example available at https://github.com/mmmarinho/icra2023_orbitalmanipulation.
Sim-to-Real Transfer for Quadrupedal Locomotion via Terrain Transformer
Venue: Conference ICRA 2023
Year: 2023

Deep reinforcement learning has recently emerged as an appealing alternative for legged locomotion over multiple terrains by training a policy in physical simulation and then transferring it to the real world (i.e., sim-to-real transfer). Despite considerable progress, the capacity and scalability of traditional neural networks are still limited, which may hinder their applications in more complex environments. In contrast, the Transformer architecture has shown its superiority in a wide range of large-scale sequence modeling tasks, including natural language processing and decision-making problems. In this paper, we propose Terrain Transformer (TERT), a high-capacity Transformer model for quadrupedal locomotion control on various terrains. Furthermore, to better leverage Transformer in sim-to-real scenarios, we present a novel two-stage training framework consisting of an offline pretraining stage and an online correction stage, which can naturally integrate Transformer with privileged training. Extensive experiments in simulation demonstrate that TERT outperforms state-of-the-art baselines on different terrains in terms of return, energy consumption and control smoothness. In further real-world validation, TERT successfully traverses nine challenging terrains, including sand pit and stair down, which can not be accomplished by strong baselines.
Design and Mechanics of Cable-Driven Rolling Diaphragm Transmission for High-Transparency Robotic Motion
Venue: Conference ICRA 2023
Year: 2023

Applications of rolling diaphragm transmissions for medical and teleoperated robotics are of great interest, due to the low friction of rolling diaphragms combined with the power density and stiffness of hydraulic transmissions. However, the stiffness-enabling pressure preloads can form a tradeoff against bearing loading in some rolling diaphragm layouts, and transmission setup can be difficult. Utilization of cable drives compliment the rolling diaphragm transmission's advantages, but maintaining cable tension is crucial for optimal and consistent performance. In this paper, a coaxial opposed rolling diaphragm layout with cable drive and an electronic transmission control system are investigated, with a focus on system reliability and scalability. Mechanical features are proposed which enable force balancing, decoupling of transmission pressure from bearing loads, and maintenance of cable tension. Key considerations and procedures for automation of transmission setup, phasing, and operation are also presented. We also present an analysis of system stiffness to identify key compliance contributors, and conduct experiments to validate prototype design performance.
Model-Based Pose Estimation of Steerable Catheters under Bi-Plane Image Feedback
Venue: Conference ICRA 2023
Year: 2023

Small catheters undergo significant torsional deflections during endovascular interventions. A key challenge in enabling robot control of these catheters is the estimation of their bending planes. This paper considers approaches for estimating these bending planes based on bi-plane image feedback. The proposed approaches attempt to minimize error between either the direct (position-based) or instantaneous (velocity-based) kinematics with the reconstructed kinematics from bi-plane image feedback. A comparison between these methods is carried out on a setup using two cameras in lieu of a bi-plane fluoroscopy setup. The results show that the position-based approach is less susceptible to segmentation noise and works best when the segment is in a non-straight configuration. These results suggest that estimation of the bending planes can be accompanied with errors under 30 degrees. Considering that the torsional buildup of these catheters can be more than 180 degrees, we believe that this method can be used for catheter control with improved safety due to the reduction of this uncertainty.
Edge-guided Multi-domain RGB-to-TIR image Translation for Training Vision Tasks with Challenging Labels
Venue: Conference ICRA 2023
Year: 2023

The insufficient number of annotated thermal infrared (TIR) image datasets not only hinders TIR image-based deep learning networks to have comparable performances to that of RGB but it also limits the supervised learning of TIR image-based tasks with challenging labels. As a remedy, we propose a modified multidomain RGB to TIR image translation model focused on edge preservation to employ annotated RGB images with challenging labels. Our proposed method not only preserves key details in the original image but also leverages the optimal TIR style code to portray accurate TIR characteristics in the translated image, when applied on both synthetic and real world RGB images. Using our translation model, we have enabled the supervised learning of deep TIR image-based optical flow estimation and object detection that ameliorated in deep TIR optical flow estimation by reduction in end point error by 56.5\% on average and the best object detection mAP of 23.9\% respectively. Our code and supplementary materials are available at https://github.com/rpmsnu/sRGB-TIR.