Actively Mapping Industrial Structures with Information Gain-Based Planning on a Quadruped Robot
Yiduo Wang, Milad Ramezani and Maurice Fallon
IEEE International Conference on Robotics and Automation
In this paper, we develop an online active mapping system to enable a quadruped robot to autonomously survey large physical structures. We describe the perception, planning and control modules needed to scan and reconstruct an object of interest, without the requirement of a prior model. The system utilises voxel space to represent the object, and iteratively determines the Next-Best-View (NBV), according to both the reconstruction itself and collision avoidance in the environment. By computing the expected information gain of a set of candidate scan locations sampled on the as-sensed terrain map, as well as the cost of reaching these candidates, the robot decides the NBV for further exploration. The robot plans an optimal path towards the NBV, avoiding obstacles and non-traversable terrain. Experimental results on both simulated and real-world environments show the capability and efficiency of our system. Finally we present a full system demonstration on the real robot, the ANYbotics ANYmal, autonomously reconstructing a building facade and an industrial structure.