# SegMap: Segment-based mapping and localization using data-driven descriptors

arXiv: https://arxiv.org/abs/1909.12837 · HTML: https://mlsanity.com/paper/1909.12837/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2019.

## Built on by (most cited first, 30 of 42)

- [LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition](https://mlsanity.com/paper/2109.08336/) (2021)
- [S-Graphs+: Real-time Localization and Mapping leveraging Hierarchical Representations](https://mlsanity.com/paper/2212.11770/) (2022)
- [MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square](https://mlsanity.com/paper/2102.03771/) (2021)
- [RoboHop: Segment-based Topological Map Representation for Open-World Visual Navigation](https://mlsanity.com/paper/2405.05792/) (2024)
- [SSC: Semantic Scan Context for Large-Scale Place Recognition](https://mlsanity.com/paper/2107.00382/) (2021)
- [Situational Graphs for Robot Navigation in Structured Indoor Environments](https://mlsanity.com/paper/2202.12197/) (2022)
- [CVTNet: A Cross-View Transformer Network for Place Recognition Using LiDAR Data](https://mlsanity.com/paper/2302.01665/) (2023)
- [SA-LOAM: Semantic-aided LiDAR SLAM with Loop Closure](https://mlsanity.com/paper/2106.11516/) (2021)
- [High-Definition Map Generation Technologies For Autonomous Driving](https://mlsanity.com/paper/2206.05400/) (2022)
- [Place recognition survey: An update on deep learning approaches](https://mlsanity.com/paper/2106.10458/) (2021)
- [MinkLoc3D-SI: 3D LiDAR place recognition with sparse convolutions, spherical coordinates, and intensity](https://mlsanity.com/paper/2112.06539/) (2021)
- [BoxGraph: Semantic Place Recognition and Pose Estimation from 3D LiDAR](https://mlsanity.com/paper/2206.15154/) (2022)
- [AutoMerge: A Framework for Map Assembling and Smoothing in City-scale Environments](https://mlsanity.com/paper/2207.06965/) (2022)
- [Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning](https://mlsanity.com/paper/2102.04960/) (2021)
- [Loop closure detection using local 3D deep descriptors](https://mlsanity.com/paper/2111.00440/) (2021)
- [Contour Context: Abstract Structural Distribution for 3D LiDAR Loop Detection and Metric Pose Estimation](https://mlsanity.com/paper/2302.06149/) (2023)
- [PSE-Match: A Viewpoint-free Place Recognition Method with Parallel Semantic Embedding](https://mlsanity.com/paper/2108.00552/) (2021)
- [RecNet: An Invertible Point Cloud Encoding through Range Image Embeddings for Multi-Robot Map Sharing and Reconstruction](https://mlsanity.com/paper/2402.02192/) (2024)
- [ForestLPR: LiDAR Place Recognition in Forests Attentioning Multiple BEV Density Images](https://mlsanity.com/paper/2503.04475/) (2025)
- [S-Graphs 2.0 -- A Hierarchical-Semantic Optimization and Loop Closure for SLAM](https://mlsanity.com/paper/2502.18044/) (2025)
- [Dynamic Semantic Occupancy Mapping using 3D Scene Flow and Closed-Form Bayesian Inference](https://mlsanity.com/paper/2108.03180/) (2021)
- [FRAME: A Modular Framework for Autonomous Map Merging: Advancements in the Field](https://mlsanity.com/paper/2404.18006/) (2024)
- [Visual Localization in 3D Maps: Comparing Point Cloud, Mesh, and NeRF Representations](https://mlsanity.com/paper/2408.11966/) (2024)
- [A Unified BEV Model for Joint Learning of 3D Local Features and Overlap Estimation](https://mlsanity.com/paper/2302.14511/) (2023)
- [Graph-based Global Robot Localization Informing Situational Graphs with Architectural Graphs](https://mlsanity.com/paper/2303.02076/) (2023)
- [That's My Point: Compact Object-centric LiDAR Pose Estimation for Large-scale Outdoor Localisation](https://mlsanity.com/paper/2403.04755/) (2024)
- [MUI-TARE: Multi-Agent Cooperative Exploration with Unknown Initial Position](https://mlsanity.com/paper/2209.10775/) (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)
- [Pose-Graph Attentional Graph Neural Network for Lidar Place Recognition](https://mlsanity.com/paper/2309.00168/) (2023)
- [DeepPointMap: Advancing LiDAR SLAM with Unified Neural Descriptors](https://mlsanity.com/paper/2312.02684/) (2023)

## Abstract (arXiv)

Precisely estimating a robot's pose in a prior, global map is a fundamental capability for mobile robotics, e.g. autonomous driving or exploration in disaster zones. This task, however, remains challenging in unstructured, dynamic environments, where local features are not discriminative enough and global scene descriptors only provide coarse information. We therefore present SegMap: a map representation solution for localization and mapping based on the extraction of segments in 3D point clouds. Working at the level of segments offers increased invariance to view-point and local structural changes, and facilitates real-time processing of large-scale 3D data. SegMap exploits a single compact data-driven descriptor for performing multiple tasks: global localization, 3D dense map reconstruction, and semantic information extraction. The performance of SegMap is evaluated in multiple urban driving and search and rescue experiments. We show that the learned SegMap descriptor has superior segment retrieval capabilities, compared to state-of-the-art handcrafted descriptors. In consequence, we achieve a higher localization accuracy and a 6% increase in recall over state-of-the-art. These segment-based localizations allow us to reduce the open-loop odometry drift by up to 50%. SegMap is open-source available along with easy to run demonstrations.