Whole-body motion planning and tracking of a mobile robot with a gimbal RGB-D camera for outdoor 3D exploration

Autonomous exploration is a fundamental problem for certain applications of unmanned ground vehicles (UGVs). Depth cameras are widely used in exploring indoor and specific outdoor environments. However, existing methods suffer from inefficient and unstable performance of exploration in open outdoor environments. This study develops a framework to achieve fast and robust three-dimensional exploration by UGVs with a gimbal camera in challenging outdoor environments. A hierarchical reactive planning approach based on a multilayered map is proposed to improve exploration efficiency. Additionally, an adaptive semantic mapping algorithm is proposed to more accurately and stably represent the environmental localization uncertainty. Localization uncertainty is integrated into the planning approach to decrease odometry drift and prevent localization failure, further enhancing the exploration robustness. A viewpoint tracking controller based on terrain-aware model predictive control is proposed to guarantee the accuracy of the planned viewpoint tracking and to further improve the robustness of localization and exploration on rough terrains. Finally, several physical-engine simulations and experiments in outdoor environments are performed. The comparisons to existing state-of-the-art approaches have verified the effectiveness of the proposed approach. The code of our system will be available at https://github.com/HITSZ-NRSL/Open3DExplorer.git.