Robust 3D Object Detection

One of the major challenges for unmanned space exploration is the latency caused by communication delays, making tasks such as docking difficult due to limited possibility for human intervention. In this paper, we address this issue by proposing an image processing technique capable of real-time, lowpower, robust, full 3D object and orientation detection. Oriented Fast and Rotated Brief (ORB) feature detector was selected as the ideal object detection technique for this study. ORB requires just one 2D reference image of the subject for performing a robust object detection, which is desirable when limited available storage onboard a spacecraft enforce constraints. Additionally, Sharif and Hölzel in a recent study illustrated ORB's robustness and invariance to orientation, rotation, and illumination variations. Thus, ORB is an ideal technique to guide a malfunctioning satellite that has no sense of orientation relative to its surroundings. ORB feature detector is a robust algorithm for detecting the subject when external factors are unpredictable, uncontrollable, and quickly changing. However, ORB is a 2D feature detector and unable to differentiate between surfaces of a subject. Via Bayesian probabilistic theorem, we proposed a new approach to help improve the confidence in detection

Optimization Techniques for Feature Detection of Orbital Debris

Space debris is exponentially increasing and becoming a pressing concern, as demonstrated by the recent malfunctioning of the European Space Agency’s Envisat and the Japanese Space Agency’s Hitomi. In this study, we propose a novel vision-based feature detection technique for the next generation of satellites to perform autonomous obstacle avoidance. The study evaluates the strength and shortcomings of thermal and visible imaging to detect an approaching subject. For this purpose, the subject was imaged in various lighting conditions and contrasts of temperature relative to the ambient temperature. Preliminary results suggested that thermal imaging at distances greater than 2m offers a higher detection accuracy in contrast to using visible imaging. However, outdoors test proved that both images offered similar poor detection readings. Fluctuations of wind and turbulence may have contributed to the low performance of the thermal images, as well as extreme illuminations respectively impacted the detection results of the visible images which were observed

A comparison of feature detection in low atmospheric pressure using thermal and visible images

Due to the proliferation of orbital debris, it is now more crucial for spacecraft to be equipped with autonomous obstacle avoidance capabilities. In this paper, we propose a novel optical navigation technique to detect nearby debris efficiently and accurately in real-time by combining feature detection results from thermal and visible images. Furthermore, to demonstrate the robustness of the proposed approach, we compare our results using only thermal or visible imagery at both sea level and at high altitude to show the accuracy gains that can be achieved. The performance of the object detection algorithm was evaluated by comparing the trade-off between object detection accuracy and overall detection runtime across different atmospheric pressures using the Oriented Fast and Rotated Brief (ORB) feature detector

Analysis of Attitude Jitter on the Performance of Feature Detection

This study explored a vision algorithm's performance during a shaker test to help reproduce the effects of vibration caused by the reaction wheels of a spacecraft. In this paper, we analyze the robustness of the feature detection technique by submitting the thermal and visible imaging cameras to sinusoidal vibrations as they simultaneously execute feature detection of the target