Illumination Robust Loop Closure Detection with the Constraint of Pose

TL;DR

This paper introduces a robust visual loop closure detection method combining illumination-invariant descriptors and odometry, improving detection accuracy and efficiency in challenging lighting conditions for SLAM.

Contribution

The paper presents a novel loop closure detection algorithm that integrates DIRD descriptors with odometry and a new distance metric, enhancing robustness against illumination changes.

Findings

Better PR-curve performance compared to SeqSLAM

Effective detection of loop closure areas in datasets

Reduced computational time for loop detection

Abstract

Background: Loop closure detection is a crucial part in robot navigation and simultaneous location and mapping (SLAM). Appearance-based loop closure detection still faces many challenges, such as illumination changes, perceptual aliasing and increasing computational complexity. Method: In this paper, we proposed a visual loop-closure detection algorithm which combines illumination robust descriptor DIRD and odometry information. The estimated pose and variance are calculated by the visual inertial odometry (VIO), then the loop closure candidate areas are found based on the distance between images. We use a new distance combing the the Euclidean distance and the Mahalanobis distance and a dynamic threshold to select the loop closure candidate areas. Finally, in loop-closure candidate areas, we do image retrieval with DIRD which is an illumination robust descriptor. Results: The proposed algorithm is evaluated on KITTI_00 and EuRoc datasets. The results show that the loop closure areas could be correctly detected and the time consumption is effectively reduced. We compare it with SeqSLAM algorithm, the proposed algorithm gets better performance on PR-curve.