GroundSLAM: A Robust Visual SLAM System for Warehouse Robots Using Ground Textures

TL;DR

GroundSLAM is a novel feature-free visual SLAM system using ground textures and a kernel cross-correlator, providing robust localization for warehouse robots on various surfaces, outperforming existing methods.

Contribution

This paper introduces GroundSLAM, the first feature-free ground-texture SLAM system utilizing kernel cross-correlation for improved robustness and efficiency in diverse environments.

Findings

Outperforms state-of-the-art localization methods

Effective on surfaces with sparse or repetitive features

Provides a new ground-texture dataset with ground-truth poses

Abstract

A robust visual localization and mapping system is essential for warehouse robot navigation, as cameras offer a more cost-effective alternative to LiDAR sensors. However, existing forward-facing camera systems often encounter challenges in dynamic environments and open spaces, leading to significant performance degradation during deployment. To address these limitations, a localization system utilizing a single downward-facing camera to capture ground textures presents a promising solution. Nevertheless, existing feature-based ground-texture localization methods face difficulties when operating on surfaces with sparse features or repetitive patterns. To address this limitation, we propose GroundSLAM, a novel feature-free and ground-texture-based simultaneous localization and mapping (SLAM) system. GroundSLAM consists of three components: feature-free visual odometry, ground-texture-based loop detection and map optimization, and map reuse. Specifically, we introduce a kernel cross-correlator (KCC) for image-level pose tracking, loop detection, and map reuse to improve localization accuracy and robustness, and incorporate adaptive pruning strategies to enhance efficiency. Due to these specific designs, GroundSLAM is able to deliver efficient and stable localization across various ground surfaces such as those with sparse features and repetitive patterns. To advance research in this area, we introduce the first ground-texture dataset with precise ground-truth poses, consisting of 131k images collected from 10 kinds of indoor and outdoor ground surfaces. Extensive experimental results show that GroundSLAM outperforms state-of-the-art methods for both indoor and outdoor localization. We release our code and dataset at https://github.com/sair-lab/GroundSLAM.