Probabilistic Height Grid Terrain Mapping for Mining Shovels using LiDAR

TL;DR

This paper presents a probabilistic height grid mapping method using LiDAR and Hidden Markov Models to accurately map changing terrain in mining environments, supporting autonomous machine operation.

Contribution

It introduces a novel terrain mapping approach that incorporates HMMs into height grids for confidence-based updates in dynamic environments.

Findings

Effective terrain mapping with confidence measures

Handling of semantic labeling and sensor pose estimation

Improved accuracy in changing terrain conditions

Abstract

This paper explores the question of creating and maintaining terrain maps in environments where the terrain changes. The specific example explored is the construction of terrain maps from 3D LiDAR measurements on an electric rope shovel. The approach extends the height grid representation of terrain to include a Hidden Markov Model in each cell, enabling confidence-based mapping of constantly changing terrain. There are inherent difficulties in this problem, including semantic labelling of the LiDAR measurements associated with machinery and determining the pose of the sensor. Solutions to both of these problems are explored. The significance of this work lies in the need for accurate terrain mapping to support autonomous machine operation.