Robust 3D Object Detection

Abstract

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