Occupancy Grid Mapping without Ray-Casting for High-resolution LiDAR Sensors

TL;DR

This paper introduces D-Map, an efficient occupancy mapping framework for high-resolution LiDAR sensors that uses depth images and an on-tree update strategy to improve computational efficiency and reduce map size, enabling real-time applications.

Contribution

The paper presents a novel occupancy mapping method that replaces ray-casting with depth images, employs an on-tree update strategy, and removes known cells to enhance efficiency and enable decremental map updates.

Findings

D-Map achieves higher efficiency than existing methods.

Maintains comparable mapping accuracy.

Demonstrates real-time performance on various platforms.

Abstract

Occupancy mapping is a fundamental component of robotic systems to reason about the unknown and known regions of the environment. This article presents an efficient occupancy mapping framework for high-resolution LiDAR sensors, termed D-Map. The framework introduces three main novelties to address the computational efficiency challenges of occupancy mapping. Firstly, we use a depth image to determine the occupancy state of regions instead of the traditional ray-casting method. Secondly, we introduce an efficient on-tree update strategy on a tree-based map structure. These two techniques avoid redundant visits to small cells, significantly reducing the number of cells to be updated. Thirdly, we remove known cells from the map at each update by leveraging the low false alarm rate of LiDAR sensors. This approach not only enhances our framework's update efficiency by reducing map size but also endows it with an interesting decremental property, which we have named D-Map. To support our design, we provide theoretical analyses of the accuracy of the depth image projection and time complexity of occupancy updates. Furthermore, we conduct extensive benchmark experiments on various LiDAR sensors in both public and private datasets. Our framework demonstrates superior efficiency in comparison with other state-of-the-art methods while maintaining comparable mapping accuracy and high memory efficiency. We demonstrate two real-world applications of D-Map for real-time occupancy mapping on a handle device and an aerial platform carrying a high-resolution LiDAR. In addition, we open-source the implementation of D-Map on GitHub to benefit society: github.com/hku-mars/D-Map.