An integrated probabilistic model for scan-matching, moving object detection and motion estimation

This paper presents a novel framework for integrating fundamental tasks in robotic navigation through a statistical inference procedure. A probabilistic model that jointly reasons about scan-matching, moving object detection and their motion estimation is developed. Scan-matching and moving object detection are two important problems for full autonomy of robotic systems in complex dynamic environments. Popular techniques for solving these problems usually address each task in turn disregarding important dependencies. The model developed here jointly reasons about these tasks by performing inference in a probabilistic graphical model. It allows different but related problems to be expressed in a single framework. The experiments demonstrate that jointly reasoning results in better estimates for both tasks compared to solving the tasks individually.