# Robust Place Recognition using an Imaging Lidar

arXiv: https://arxiv.org/abs/2103.02111 · HTML: https://mlsanity.com/paper/2103.02111/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 3 Mar 2021.

## Builds on (most cited first, 7 of 7)

- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping](https://mlsanity.com/paper/2007.00258/) (2020)
- [LiDAR Iris for Loop-Closure Detection](https://mlsanity.com/paper/1912.03825/) (2019)
- [Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection](https://mlsanity.com/paper/2003.05656/) (2020)
- [Learning to Localize Using a LiDAR Intensity Map](https://mlsanity.com/paper/2012.10902/) (2020)
- [Local Descriptor for Robust Place Recognition using LiDAR Intensity](https://mlsanity.com/paper/1811.12646/) (2018)

## Built on by (most cited first, 9 of 9)

- [KISS-Matcher: Fast and Robust Point Cloud Registration Revisited](https://mlsanity.com/paper/2409.15615/) (2024)
- [General Place Recognition Survey: Towards the Real-world Autonomy Age](https://mlsanity.com/paper/2209.04497/) (2022)
- [AutoMerge: A Framework for Map Assembling and Smoothing in City-scale Environments](https://mlsanity.com/paper/2207.06965/) (2022)
- [Real-Time Simultaneous Localization and Mapping with LiDAR intensity](https://mlsanity.com/paper/2301.09257/) (2023)
- [DSC: Deep Scan Context Descriptor for Large-Scale Place Recognition](https://mlsanity.com/paper/2111.13838/) (2021)
- [A Survey on Visual Map Localization Using LiDARs and Cameras](https://mlsanity.com/paper/2208.03376/) (2022)
- [RE-TRIP : Reflectivity Instance Augmented Triangle Descriptor for 3D Place Recognition](https://mlsanity.com/paper/2505.16165/) (2025)
- [IFTD: Image Feature Triangle Descriptor for Loop Detection in Driving Scenes](https://mlsanity.com/paper/2406.07937/) (2024)
- [Cross-Modal Visual Relocalization in Prior LiDAR Maps Utilizing Intensity Textures](https://mlsanity.com/paper/2412.01299/) (2024)

## Abstract (arXiv)

We propose a methodology for robust, real-time place recognition using an imaging lidar, which yields image-quality high-resolution 3D point clouds. Utilizing the intensity readings of an imaging lidar, we project the point cloud and obtain an intensity image. ORB feature descriptors are extracted from the image and encoded into a bag-of-words vector. The vector, used to identify the point cloud, is inserted into a database that is maintained by DBoW for fast place recognition queries. The returned candidate is further validated by matching visual feature descriptors. To reject matching outliers, we apply PnP, which minimizes the reprojection error of visual features' positions in Euclidean space with their correspondences in 2D image space, using RANSAC. Combining the advantages from both camera and lidar-based place recognition approaches, our method is truly rotation-invariant, and can tackle reverse revisiting and upside down revisiting. The proposed method is evaluated on datasets gathered from a variety of platforms over different scales and environments. Our implementation and datasets are available at https://git.io/image-lidar