# Any Way You Look At It: Semantic Crossview Localization and Mapping with LiDAR

arXiv: https://arxiv.org/abs/2203.08925 · HTML: https://mlsanity.com/paper/2203.08925/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2022.

## Built on by (most cited first, 12 of 12)

- [GAMa: Cross-view Video Geo-localization](https://mlsanity.com/paper/2207.02431/) (2022)
- [Satellite Image Based Cross-view Localization for Autonomous Vehicle](https://mlsanity.com/paper/2207.13506/) (2022)
- [View Consistent Purification for Accurate Cross-View Localization](https://mlsanity.com/paper/2308.08110/) (2023)
- [Graph-based Global Robot Localization Informing Situational Graphs with Architectural Graphs](https://mlsanity.com/paper/2303.02076/) (2023)
- [SGFormer: Satellite-Ground Fusion for 3D Semantic Scene Completion](https://mlsanity.com/paper/2503.16825/) (2025)
- [View-Invariant Localization using Semantic Objects in Changing Environments](https://mlsanity.com/paper/2209.14426/) (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)
- [AGL-NET: Aerial-Ground Cross-Modal Global Localization with Varying Scales](https://mlsanity.com/paper/2404.03187/) (2024)
- [A Survey on Visual Map Localization Using LiDARs and Cameras](https://mlsanity.com/paper/2208.03376/) (2022)
- [Sparse Semantic Map-Based Monocular Localization in Traffic Scenes Using Learned 2D-3D Point-Line Correspondences](https://mlsanity.com/paper/2210.04543/) (2022)
- [Semi-distributed Cross-modal Air-Ground Relative Localization](https://mlsanity.com/paper/2511.06749/) (2025)
- [GeoISF: Instance Semantic Forest Inspired Large-Scale Cross-View Geo-Localization via Ground LiDAR-to-Satellite Image](https://mlsanity.com/paper/2606.28371/) (2026)

## Abstract (arXiv)

Currently, GPS is by far the most popular global localization method. However, it is not always reliable or accurate in all environments. SLAM methods enable local state estimation but provide no means of registering the local map to a global one, which can be important for inter-robot collaboration or human interaction. In this work, we present a real-time method for utilizing semantics to globally localize a robot using only egocentric 3D semantically labelled LiDAR and IMU as well as top-down RGB images obtained from satellites or aerial robots. Additionally, as it runs, our method builds a globally registered, semantic map of the environment. We validate our method on KITTI as well as our own challenging datasets, and show better than 10 meter accuracy, a high degree of robustness, and the ability to estimate the scale of a top-down map on the fly if it is initially unknown.