# DeLiO: Decoupled LiDAR Odometry

## TL;DR

DeLiO introduces a novel LiDAR odometry method that fully decouples rotation and translation estimation, improving accuracy by separately estimating rotation via surface normals and then translation.

## Contribution

The paper presents the first approach to completely decouple rotation and translation estimation in LiDAR odometry, using surface normals for rotation and line cloud methods for translation.

## Key findings

- Outperforms state-of-the-art algorithms on KITTI dataset
- Accurately estimates rotation by surface normal tracking
- Efficiently computes translation after rotation correction

## Abstract

Most LiDAR odometry algorithms estimate the transformation between two consecutive frames by estimating the rotation and translation in an intervening fashion. In this paper, we propose our Decoupled LiDAR Odometry (DeLiO), which -- for the first time -- decouples the rotation estimation completely from the translation estimation. In particular, the rotation is estimated by extracting the surface normals from the input point clouds and tracking their characteristic pattern on a unit sphere. Using this rotation the point clouds are unrotated so that the underlying transformation is pure translation, which can be easily estimated using a line cloud approach. An evaluation is performed on the KITTI dataset and the results are compared against state-of-the-art algorithms.