# A Depth-Based Hybrid Approach for Safe Flight Corridor Generation in Memoryless Planning
## 1 Introduction  
In recent years, while significant advancements in aerial robotics have been opening up various applications across industries, including inspection, search and rescue, surveillance, exploration, and more [1, 2, 3, 4], autonomous navigation in complex environments still presents challenges, especially in cluttered and obstacle-dense environments. The localization and mapping of aerial robots can be subject to state estimation error and sensor fusion drift [5, 6, 7]. On the other hand, maintaining a high-resolution map in real time demands high computational resources [8]. To avoid those limitations, existing research has explored computationally efficient approaches that utilize only the most recent sensor data and do not maintain any global map, namely mapless [9, 10, 11] or memoryless planning [12, 13]. Memoryless planning is similar to classical sensor-based planning [14, 15], which does not have complete knowledge of the environment a priori. The robot relies on its integrated sensors for local perception and planning. These approaches are inspired by human behavior, which cannot remember environmental features over a long time but can perceive the local risks or uncertainties and quickly react to them.  
However, due to its inherent short observation horizon, the overall planning performance is degraded, and the computational efficiency may not be at its best. Specifically, as a local planning algorithm, memoryless planners do not guarantee planning completeness since they can be trapped in a dead end [13]. Learning-based methods [16, 17, 18], a typicaltype of memoryless algorithms, suffer from prediction uncertainty, which can lead to collisions with obstacles, especially end-to-end learning [19]. On the other hand, classical local planners usually utilize sampling techniques in the local occupancy grid [20; 21; 22; 23; 24] or in sensor raw data [12], consuming considerable computing resources due to local occupancy fusion or a sampling job.  
We aim to design a method to combine the advantages of both approaches and overcome their weaknesses. This work focuses on delivering a safe flight corridor (SFC) generator, utilizing a learning-based object-detection engine. We exploit robustness from a learning model to generate explicitly separated polyhedral corridors, which are beneficial for path planning. We then adopt a depth-based verification procedure to discard SFC that would result in a collision. Although this step can be over-conservative, it eliminates false predictions from a learning object-detection model, ensuring our generated SFCs are collision-free.  
Our contributions can be summarized as follows:  
1. We propose a hybrid safe flight corridor generation technique that utilizes only depth images. It produces less-overlapping corridors compared to a depth-based inflating method.
2. We propose a memoryless planner employing the above-generated corridors to navigate a quadrotor autonomously through an obstacle-dense environment to a destination without employing any map or external global planners. The entire planning system can run in real time with fully onboard sensing and computation.
3. We implemented and validated the proposed method on an actual fully autonomous quadrotor system. Real-world flight tests show that our methods can effectively navigate a quadrotor through cluttered scenarios.