Actively Mapping Industrial Structures with Information Gain-Based Planning on a Quadruped Robot

TL;DR

This paper presents an online active mapping system enabling a quadruped robot to autonomously survey and reconstruct large structures by actively selecting viewpoints based on information gain, without prior models.

Contribution

It introduces a novel information gain-based planning approach for autonomous, collision-free mapping on a quadruped robot without prior environment knowledge.

Findings

Effective reconstruction of large structures demonstrated in real-world tests.

System operates efficiently in both simulated and real environments.

Autonomous navigation and mapping achieved without prior environment models.

Abstract

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 requiring a prior model. The system builds a voxel representation of the object, and iteratively determines the Next-Best-View (NBV) to extend the representation, according to both the reconstruction itself and to avoid collisions with 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 un-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.