RE-TRIP : Reflectivity Instance Augmented Triangle Descriptor for 3D Place Recognition

TL;DR

This paper introduces RE-TRIP, a novel 3D place recognition descriptor that combines geometric and reflectivity data from LiDAR to improve robustness in challenging environments, outperforming existing methods.

Contribution

The paper proposes RE-TRIP, a new descriptor that integrates reflectivity with geometric measurements, along with methods for keypoint extraction, segmentation, matching, and loop verification.

Findings

RE-TRIP outperforms state-of-the-art methods on public datasets.

Incorporating reflectivity improves robustness in dynamic and ambiguous environments.

The method is effective across diverse scenarios like urban areas and corridors.

Abstract

While most people associate LiDAR primarily with its ability to measure distances and provide geometric information about the environment (via point clouds), LiDAR also captures additional data, including reflectivity or intensity values. Unfortunately, when LiDAR is applied to Place Recognition (PR) in mobile robotics, most previous works on LiDAR-based PR rely only on geometric measurements, neglecting the additional reflectivity information that LiDAR provides. In this paper, we propose a novel descriptor for 3D PR, named RE-TRIP (REflectivity-instance augmented TRIangle descriPtor). This new descriptor leverages both geometric measurements and reflectivity to enhance robustness in challenging scenarios such as geometric degeneracy, high geometric similarity, and the presence of dynamic objects. To implement RE-TRIP in real-world applications, we further propose (1) a keypoint extraction method, (2) a key instance segmentation method, (3) a RE-TRIP matching method, and (4) a reflectivity-combined loop verification method. Finally, we conduct a series of experiments to demonstrate the effectiveness of RE-TRIP. Applied to public datasets (i.e., HELIPR, FusionPortable) containing diverse scenarios such as long corridors, bridges, large-scale urban areas, and highly dynamic environments -- our experimental results show that the proposed method outperforms existing state-of-the-art methods in terms of Scan Context, Intensity Scan Context, and STD.