# Intensity-SLAM: Intensity Assisted Localization and Mapping for Large Scale Environment

arXiv: https://arxiv.org/abs/2102.03798 · HTML: https://mlsanity.com/paper/2102.03798/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2021.

## Built on by (most cited first, 6 of 6)

- [Real-Time Simultaneous Localization and Mapping with LiDAR intensity](https://mlsanity.com/paper/2301.09257/) (2023)
- [TREND: Unsupervised 3D Representation Learning via Temporal Forecasting for LiDAR Perception](https://mlsanity.com/paper/2412.03054/) (2024)
- [JiSAM: Alleviate Labeling Burden and Corner Case Problems in Autonomous Driving via Minimal Real-World Data](https://mlsanity.com/paper/2503.08422/) (2025)
- [RE-TRIP : Reflectivity Instance Augmented Triangle Descriptor for 3D Place Recognition](https://mlsanity.com/paper/2505.16165/) (2025)
- [LR-SGS: Robust LiDAR-Reflectance-Guided Salient Gaussian Splatting for Self-Driving Scene Reconstruction](https://mlsanity.com/paper/2603.12647/) (2026)
- [Cross-Modal Visual Relocalization in Prior LiDAR Maps Utilizing Intensity Textures](https://mlsanity.com/paper/2412.01299/) (2024)

## Abstract (arXiv)

Simultaneous Localization And Mapping (SLAM) is a task to estimate the robot location and to reconstruct the environment based on observation from sensors such as LIght Detection And Ranging (LiDAR) and camera. It is widely used in robotic applications such as autonomous driving and drone delivery. Traditional LiDAR-based SLAM algorithms mainly leverage the geometric features from the scene context, while the intensity information from LiDAR is ignored. Some recent deep-learning-based SLAM algorithms consider intensity features and train the pose estimation network in an end-to-end manner. However, they require significant data collection effort and their generalizability to environments other than the trained one remains unclear. In this paper we introduce intensity features to a SLAM system. And we propose a novel full SLAM framework that leverages both geometry and intensity features. The proposed SLAM involves both intensity-based front-end odometry estimation and intensity-based back-end optimization. Thorough experiments are performed including both outdoor autonomous driving and indoor warehouse robot manipulation. The results show that the proposed method outperforms existing geometric-only LiDAR SLAM methods.