LRSLAM: Low-rank Representation of Signed Distance Fields in Dense Visual SLAM System

TL;DR

LRSLAM introduces a low-rank tensor decomposition approach for dense visual SLAM, significantly improving efficiency, scalability, and accuracy in large-scale indoor scene reconstruction and localization tasks.

Contribution

The paper presents a novel low-rank tensor decomposition method for dense visual SLAM, reducing computational costs and memory usage while enhancing convergence and accuracy.

Findings

Outperforms existing methods in parameter efficiency

Achieves faster processing times

Maintains high reconstruction and localization accuracy

Abstract

Simultaneous Localization and Mapping (SLAM) has been crucial across various domains, including autonomous driving, mobile robotics, and mixed reality. Dense visual SLAM, leveraging RGB-D camera systems, offers advantages but faces challenges in achieving real-time performance, robustness, and scalability for large-scale scenes. Recent approaches utilizing neural implicit scene representations show promise but suffer from high computational costs and memory requirements. ESLAM introduced a plane-based tensor decomposition but still struggled with memory growth. Addressing these challenges, we propose a more efficient visual SLAM model, called LRSLAM, utilizing low-rank tensor decomposition methods. Our approach, leveraging the Six-axis and CP decompositions, achieves better convergence rates, memory efficiency, and reconstruction/localization quality than existing state-of-the-art approaches. Evaluation across diverse indoor RGB-D datasets demonstrates LRSLAM's superior performance in terms of parameter efficiency, processing time, and accuracy, retaining reconstruction and localization quality. Our code will be publicly available upon publication.