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

arXiv: https://arxiv.org/abs/2505.16165 · HTML: https://mlsanity.com/paper/2505.16165/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 22 May 2025.

## Builds on (most cited first, 14 of 14)

- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping](https://mlsanity.com/paper/2007.00258/) (2020)
- [Scan Context++: Structural Place Recognition Robust to Rotation and Lateral Variations in Urban Environments](https://mlsanity.com/paper/2109.13494/) (2021)
- [Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection](https://mlsanity.com/paper/2003.05656/) (2020)
- [HeLiPR: Heterogeneous LiDAR Dataset for inter-LiDAR Place Recognition under Spatiotemporal Variations](https://mlsanity.com/paper/2309.14590/) (2023)
- [STD: Stable Triangle Descriptor for 3D place recognition](https://mlsanity.com/paper/2209.12435/) (2022)
- [FusionPortable: A Multi-Sensor Campus-Scene Dataset for Evaluation of Localization and Mapping Accuracy on Diverse Platforms](https://mlsanity.com/paper/2208.11865/) (2022)
- [Robust Place Recognition using an Imaging Lidar](https://mlsanity.com/paper/2103.02111/) (2021)
- [Outram: One-shot Global Localization via Triangulated Scene Graph and Global Outlier Pruning](https://mlsanity.com/paper/2309.08914/) (2023)
- [Intensity-SLAM: Intensity Assisted Localization and Mapping for Large Scale Environment](https://mlsanity.com/paper/2102.03798/) (2021)
- [Visual Place Recognition using LiDAR Intensity Information](https://mlsanity.com/paper/2103.09605/) (2021)
- [COIN-LIO: Complementary Intensity-Augmented LiDAR Inertial Odometry](https://mlsanity.com/paper/2310.01235/) (2023)
- [Real-Time Simultaneous Localization and Mapping with LiDAR intensity](https://mlsanity.com/paper/2301.09257/) (2023)
- [Tightly Coupled Range Inertial Localization on a 3D Prior Map Based on Sliding Window Factor Graph Optimization](https://mlsanity.com/paper/2402.05540/) (2024)

## Built on by (most cited first, 1 of 1)

- [TreeLoc: 6-DoF LiDAR Global Localization in Forests via Inter-Tree Geometric Matching](https://mlsanity.com/paper/2602.01501/) (2026)

## Abstract (arXiv)

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.