# Learning an Overlap-based Observation Model for 3D LiDAR Localization

arXiv: https://arxiv.org/abs/2105.11717 · HTML: https://mlsanity.com/paper/2105.11717/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2021.

## Built on by (most cited first, 8 of 8)

- [NeRF-LOAM: Neural Implicit Representation for Large-Scale Incremental LiDAR Odometry and Mapping](https://mlsanity.com/paper/2303.10709/) (2023)
- [SeqOT: A Spatial-Temporal Transformer Network for Place Recognition Using Sequential LiDAR Data](https://mlsanity.com/paper/2209.07951/) (2022)
- [InsMOS: Instance-Aware Moving Object Segmentation in LiDAR Data](https://mlsanity.com/paper/2303.03909/) (2023)
- [IR-MCL: Implicit Representation-Based Online Global Localization](https://mlsanity.com/paper/2210.03113/) (2022)
- [Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments](https://mlsanity.com/paper/2208.07364/) (2022)
- [Why semantics matters: A deep study on semantic particle-filtering localization in a LiDAR semantic pole-map](https://mlsanity.com/paper/2305.14038/) (2023)
- [Reliable-loc: Robust sequential LiDAR global localization in large-scale street scenes based on verifiable cues](https://mlsanity.com/paper/2411.07815/) (2024)
- [CoFi: Coarse-to-Fine ICP for LiDAR Localization in an Efficient Long-lasting Point Cloud Map](https://mlsanity.com/paper/2110.10194/) (2021)

## Abstract (arXiv)

Localization is a crucial capability for mobile robots and autonomous cars. In this paper, we address learning an observation model for Monte-Carlo localization using 3D LiDAR data. We propose a novel, neural network-based observation model that computes the expected overlap of two 3D LiDAR scans. The model predicts the overlap and yaw angle offset between the current sensor reading and virtual frames generated from a pre-built map. We integrate this observation model into a Monte-Carlo localization framework and tested it on urban datasets collected with a car in different seasons. The experiments presented in this paper illustrate that our method can reliably localize a vehicle in typical urban environments. We furthermore provide comparisons to a beam-end point and a histogram-based method indicating a superior global localization performance of our method with fewer particles.