Active View Planning for Visual SLAM in Outdoor Environments Based on Continuous Information Modeling

TL;DR

This paper introduces a novel active view planning method for outdoor visual SLAM that uses continuous information modeling and receding horizon optimization to improve localization accuracy and robustness in challenging environments.

Contribution

It proposes a new environmental information representation and an optimization framework for selecting informative viewpoints in outdoor visual SLAM.

Findings

Enhanced localization robustness demonstrated in outdoor experiments.

Improved accuracy of SLAM in texture-sparse environments.

Effective real-time viewpoint optimization achieved.

Abstract

The visual simultaneous localization and mapping(vSLAM) is widely used in GPS-denied and open field environments for ground and surface robots. However, due to the frequent perception failures derived from lacking visual texture or the {swing} of robot view direction on rough terrains, the accuracy and robustness of vSLAM are still to be enhanced. The study develops a novel view planning approach of actively perceiving areas with maximal information to address the mentioned problem; a gimbal camera is used as the main sensor. Firstly, a map representation based on feature distribution-weighted Fisher information is proposed to completely and effectively represent environmental information richness. With the map representation, a continuous environmental information model is further established to convert the discrete information space into a continuous one for numerical optimization in real-time. Subsequently, the receding horizon optimization is utilized to obtain the optimal informative viewpoints with simultaneously considering the robotic perception, exploration and motion cost based on the continuous environmental model. Finally, several simulations and outdoor experiments are performed to verify the improvement of localization robustness and accuracy by the proposed approach.