Feature-constrained Active Visual SLAM for Mobile Robot Navigation

This paper focuses on tracking failure avoidance during vision-based navigation to a desired goal in unknown environments. While using feature-based Visual Simultaneous Localization and Mapping (VSLAM), continuous identification and association of map points are required during motion. Thus, we discuss a motion planning framework that takes into account sensory constraints for a reliable navigation. We use information available in the SLAM and propose a data-driven approach to predict the number of map points associated in a given pose. Then, a distance-optimal path planner utilizes the model to constrain paths such that the number of associated map points in each pose is above a threshold. We also include an online mapping of the environment for collision avoidance. Overall, we propose an iterative motion planning framework that enables real-time replanning after the acquisition of more information. Experiments in two environments demonstrate the performance of the proposed framework.