D-LIO: 6DoF Direct LiDAR-Inertial Odometry based on Simultaneous Truncated Distance Field Mapping

TL;DR

This paper introduces D-LIO, a novel 6DoF LiDAR-inertial odometry method that uses simultaneous truncated distance field mapping on CPU, enabling accurate, feature-free, real-time environment mapping suitable for various robotics applications.

Contribution

The paper proposes a new online LiDAR-inertial odometry approach based on Fast Truncated Distance Fields, eliminating feature tracking and enabling environment mapping for diverse scenarios.

Findings

Achieves accuracy comparable or superior to state-of-the-art methods.

Provides real-time environment mapping independent of map size.

Demonstrates effectiveness on open datasets and various environments.

Abstract

This paper presents a new approach for 6DoF Direct LiDAR-Inertial Odometry (D-LIO) based on the simultaneous mapping of truncated distance fields on CPU. Such continuous representation (in the vicinity of the points) enables working with raw 3D LiDAR data online, avoiding the need of LiDAR feature selection and tracking, simplifying the odometry pipeline and easily generalizing to many scenarios. The method is based on the proposed Fast Truncated Distance Field (Fast-TDF) method as a convenient tool to represent the environment. Such representation enables i) solving the LiDAR point-cloud registration as a nonlinear optimization process without the need of selecting/tracking LiDAR features in the input data, ii) simultaneously producing an accurate truncated distance field map of the environment, and iii) updating such map at constant time independently of its size. The approach is tested using open datasets, aerial and ground. It is also benchmarked against other state-of-the-art odometry approaches, demonstrating the same or better level of accuracy with the added value of an online-generated TDF representation of the environment, that can be used for other robotics tasks as planning or collision avoidance. The source code is publicly available at https://anonymous.4open.science/r/D-LIO