Stochastic triangular mesh mapping: A terrain mapping technique for autonomous mobile robots

TL;DR

This paper introduces the stochastic triangular mesh (STM) mapping technique, a continuous, incrementally updatable 2.5-D surface representation for autonomous robots that effectively models environment structure and reduces uncertainty effects.

Contribution

The paper presents a novel STM mapping method that models environment surfaces with continuous triangular meshes, enabling efficient, incremental updates and improved accuracy over existing elevation maps.

Findings

STM maps are more accurate than standard elevation maps.

The method effectively incorporates measurement and pose uncertainty.

STM maps can utilize LiDAR and stereo camera data.

Abstract

For mobile robots to operate autonomously in general environments, perception is required in the form of a dense metric map. For this purpose, we present the stochastic triangular mesh (STM) mapping technique: a 2.5-D representation of the surface of the environment using a continuous mesh of triangular surface elements, where each surface element models the mean plane and roughness of the underlying surface. In contrast to existing mapping techniques, a STM map models the structure of the environment by ensuring a continuous model, while also being able to be incrementally updated with linear computational cost in the number of measurements. We reduce the effect of uncertainty in the robot pose (position and orientation) by using landmark-relative submaps. The uncertainty in the measurements and robot pose are accounted for by the use of Bayesian inference techniques during the map update. We demonstrate that a STM map can be used with sensors that generate point measurements, such as light detection and ranging (LiDAR) sensors and stereo cameras. We show that a STM map is a more accurate model than the only comparable online surface mapping technique$\unicode{x2014}$a standard elevation map$\unicode{x2014}$and we also provide qualitative results on practical datasets.