A motion segmentation technique for mobile robots using probabilistic models

Abstract

A dynamic environment can be challenging for a robot to navigate; it should avoid collisions with objects while determining its position in its environment (localisation). Thus, it is necessary for a mobile robot to take measurements of its environment, such as features from camera images, to determine whether objects are static or dynamic (motion segmentation). This is difficult to do as knowledge of static objects is required for localisation which is then used to track the trajectories of dynamic objects. This paper proposes a motion segmentation technique that classifies objects as static or dynamic by measuring the change in distance between them across many time steps; this removes the need for localisation information. The technique is adapted from a probabilistic method for outlier removal and existing motion segmentation techniques. A simple, 1D environment is simulated to show proof of concept. Additionally, a few strategies for PGM model construction are investigated where the results show a clear relationship between accuracy and computational times.