BeautyMap: Binary-Encoded Adaptable Ground Matrix for Dynamic Points Removal in Global Maps

TL;DR

BeautyMap is a novel binary-encoded matrix method that efficiently removes dynamic points from global maps, improving accuracy and computational efficiency in static environment representation.

Contribution

It introduces a binary-encoded matrix approach combined with hierarchical segmentation and visibility considerations for dynamic point removal in global maps.

Findings

Outperforms existing methods in accuracy and efficiency

Effectively preserves static environment features

Handles terrain variations and visibility issues

Abstract

Global point clouds that correctly represent the static environment features can facilitate accurate localization and robust path planning. However, dynamic objects introduce undesired ghost tracks that are mixed up with the static environment. Existing dynamic removal methods normally fail to balance the performance in computational efficiency and accuracy. In response, we present BeautyMap to efficiently remove the dynamic points while retaining static features for high-fidelity global maps. Our approach utilizes a binary-encoded matrix to efficiently extract the environment features. With a bit-wise comparison between matrices of each frame and the corresponding map region, we can extract potential dynamic regions. Then we use coarse to fine hierarchical segmentation of the $z$-axis to handle terrain variations. The final static restoration module accounts for the range-visibility of each single scan and protects static points out of sight. Comparative experiments underscore BeautyMap's superior performance in both accuracy and efficiency against other dynamic points removal methods. The code is open-sourced at https://github.com/MKJia/BeautyMap.