AirSLAM: An Efficient and Illumination-Robust Point-Line Visual SLAM System

Abstract

In this paper, we present an efficient visual SLAM system designed to tackle both short-term and long-term illumination challenges. Our system adopts a hybrid approach that combines deep learning techniques for feature detection and matching with traditional backend optimization methods. Specifically, we propose a unified convolutional neural network (CNN) that simultaneously extracts keypoints and structural lines. These features are then associated, matched, triangulated, and optimized in a coupled manner. Additionally, we introduce a lightweight relocalization pipeline that reuses the built map, where keypoints, lines, and a structure graph are used to match the query frame with the map. To enhance the applicability of the proposed system to real-world robots, we deploy and accelerate the feature detection and matching networks using C++ and NVIDIA TensorRT. Extensive experiments conducted on various datasets demonstrate that our system outperforms other state-of-the-art visual SLAM systems in illumination-challenging environments. Efficiency evaluations show that our system can run at a rate of 73Hz on a PC and 40Hz on an embedded platform. Our implementation is open-sourced: https://github.com/sair-lab/AirSLAM.

20 pages, 15 figures, 9 tables

References in corpus (8)

• ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM
• RTAB-Map as an Open-Source Lidar and Visual SLAM Library for Large-Scale and Long-Term Online Operation
• LIFT-SLAM: a deep-learning feature-based monocular visual SLAM method
• maplab 2.0 -- A Modular and Multi-Modal Mapping Framework
• AirVO: An Illumination-Robust Point-Line Visual Odometry
• Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning
• Learning Regional Attraction for Line Segment Detection
• Multi-Session Visual SLAM for Illumination Invariant Re-Localization in Indoor Environments

Cited by in corpus (3)

• Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy
• Anti-Degeneracy Scheme for Lidar SLAM based on Particle Filter in Geometry Feature-Less Environments
• Enhancing Scene Coordinate Regression with Efficient Keypoint Detection and Sequential Information