# Visual Place Recognition using LiDAR Intensity Information

arXiv: https://arxiv.org/abs/2103.09605 · HTML: https://mlsanity.com/paper/2103.09605/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2021.

## Built on by (most cited first, 5 of 5)

- [General Place Recognition Survey: Towards the Real-world Autonomy Age](https://mlsanity.com/paper/2209.04497/) (2022)
- [LiDAR-guided object search and detection in Subterranean Environments](https://mlsanity.com/paper/2210.14997/) (2022)
- [Real-Time Simultaneous Localization and Mapping with LiDAR intensity](https://mlsanity.com/paper/2301.09257/) (2023)
- [VAPOR: Legged Robot Navigation in Outdoor Vegetation Using Offline Reinforcement Learning](https://mlsanity.com/paper/2309.07832/) (2023)
- [RE-TRIP : Reflectivity Instance Augmented Triangle Descriptor for 3D Place Recognition](https://mlsanity.com/paper/2505.16165/) (2025)

## Abstract (arXiv)

Robots and autonomous systems need to know where they are within a map to navigate effectively. Thus, simultaneous localization and mapping or SLAM is a common building block of robot navigation systems. When building a map via a SLAM system, robots need to re-recognize places to find loop closure and reduce the odometry drift. Image-based place recognition received a lot of attention in computer vision, and in this work, we investigate how such approaches can be used for 3D LiDAR data. Recent LiDAR sensors produce high-resolution 3D scans in combination with comparably stable intensity measurements. Through a cylindrical projection, we can turn this information into a panoramic image. As a result, we can apply techniques from visual place recognition to LiDAR intensity data. The question of how well this approach works in practice has not been answered so far. This paper provides an analysis of how such visual techniques can be with LiDAR data, and we provide an evaluation on different datasets. Our results suggest that this form of place recognition is possible and an effective means for determining loop closures.