# DLL: Direct LIDAR Localization. A map-based localization approach for aerial robots

arXiv: https://arxiv.org/abs/2103.06112 · HTML: https://mlsanity.com/paper/2103.06112/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2021.

## Built on by (most cited first, 4 of 4)

- [LATITUDE: Robotic Global Localization with Truncated Dynamic Low-pass Filter in City-scale NeRF](https://mlsanity.com/paper/2209.08498/) (2022)
- [CMSG Cross-Media Semantic-Graph Feature Matching Algorithm for Autonomous Vehicle Relocalization](https://mlsanity.com/paper/2305.08318/) (2023)
- [Incorporating GNSS Information with LIDAR-Inertial Odometry for Accurate Land-Vehicle Localization](https://mlsanity.com/paper/2503.23199/) (2025)
- [G-EDF-Loc: 3D Continuous Gaussian Distance Field for Robust Gradient-Based 6DoF Localization](https://mlsanity.com/paper/2604.04525/) (2026)

## Abstract (arXiv)

This paper presents DLL, a fast direct map-based localization technique using 3D LIDAR for its application to aerial robots. DLL implements a point cloud to map registration based on non-linear optimization of the distance of the points and the map, thus not requiring features, neither point correspondences. Given an initial pose, the method is able to track the pose of the robot by refining the predicted pose from odometry. Through benchmarks using real datasets and simulations, we show how the method performs much better than Monte-Carlo localization methods and achieves comparable precision to other optimization-based approaches but running one order of magnitude faster. The method is also robust under odometric errors. The approach has been implemented under the Robot Operating System (ROS), and it is publicly available.