Deep Semantic Classification for 3D LiDAR Data

Robots are expected to operate autonomously in dynamic environments. Understanding the underlying dynamic characteristics of objects is a key enabler for achieving this goal. In this paper, we propose a method for pointwise semantic classification of 3D LiDAR data into three classes: non-movable, movable and dynamic. We concentrate on understanding these specific semantics because they characterize important information required for an autonomous system. Non-movable points in the scene belong to unchanging segments of the environment, whereas the remaining classes corresponds to the changing parts of the scene. The difference between the movable and dynamic class is their motion state. The dynamic points can be perceived as moving, whereas movable objects can move, but are perceived as static. To learn the distinction between movable and non-movable points in the environment, we introduce an approach based on deep neural network and for detecting the dynamic points, we estimate pointwise motion. We propose a Bayes filter framework for combining the learned semantic cues with the motion cues to infer the required semantic classification. In extensive experiments, we compare our approach with other methods on a standard benchmark dataset and report competitive results in comparison to the existing state-of-the-art. Furthermore, we show an improvement in the classification of points by combining the semantic cues retrieved from the neural network with the motion cues.Comment: 8 pages to be published in IROS 201

Real-Time Satellite Component Recognition with YOLO-V5

With the increasing risk of collisions with space debris and the growing interest in on-orbit servicing, the ability to autonomously capture non-cooperative, tumbling target objects remains an unresolved challenge. To accomplish this task, characterizing and classifying satellite components is critical to the success of the mission. This paper focuses on using machine vision by a small satellite to perform image classification based on locating and identifying satellite components such as satellite bodies, solar panels or antennas. The classification and component detection approach is based on “You Only Look Once” (YOLO) V5, which uses Neural Networks to identify the satellite components. The training dataset includes images of real and virtual satellites and additional preprocessed images to increase the effectiveness of the algorithm. The weights obtained from the algorithm are then used in a spacecraft motion dynamics and orbital lighting simulator to test classification and detection performance. Each test case entails a different approach path of the chaser satellite to the target satellite, a different attitude motion of the target satellite, and different lighting conditions to mimic that of the Sun. Initial results indicate that once trained, the YOLO V5 approach is able to effectively process an input camera feed to solve satellite classification and component detection problems in real-time within the limitations of flight computers

Performance Study of YOLOv5 and Faster R-CNN for Autonomous Navigation around Non-Cooperative Targets

Autonomous navigation and path-planning around non-cooperative space objects is an enabling technology for on-orbit servicing and space debris removal systems. The navigation task includes the determination of target object motion, the identification of target object features suitable for grasping, and the identification of collision hazards and other keep-out zones. Given this knowledge, chaser spacecraft can be guided towards capture locations without damaging the target object or without unduly the operations of a servicing target by covering up solar arrays or communication antennas. One way to autonomously achieve target identification, characterization and feature recognition is by use of artificial intelligence algorithms. This paper discusses how the combination of cameras and machine learning algorithms can achieve the relative navigation task. The performance of two deep learning-based object detection algorithms, Faster Region-based Convolutional Neural Networks (R-CNN) and You Only Look Once (YOLOv5), is tested using experimental data obtained in formation flight simulations in the ORION Lab at Florida Institute of Technology. The simulation scenarios vary the yaw motion of the target object, the chaser approach trajectory, and the lighting conditions in order to test the algorithms in a wide range of realistic and performance limiting situations. The data analyzed include the mean average precision metrics in order to compare the performance of the object detectors. The paper discusses the path to implementing the feature recognition algorithms and towards integrating them into the spacecraft Guidance Navigation and Control system.Comment: 12 pages, 10 figures, 9 tables, IEEE Aerospace Conference 202