D-LIO: 6DoF Direct LiDAR-Inertial Odometry Based on Simultaneous Truncated Distance Field Mapping
Lucia Coto-Elena, Jose Enrique Maese, Luis Merino, Fernando Caballero
AI summary
Problem
Traditional LiDAR odometry faces high computational costs with dense point clouds and fails in feature-sparse environments, while existing distance field mapping methods are restricted to small scales or require heavy GPU resources.
Approach
The method introduces a Fast Truncated Distance Field (Fast-TDF) using binary masks and bitwise operations to represent the environment, enabling direct point-cloud registration and simultaneous map updates in constant time on a CPU without explicit feature tracking.
Key results
- Real-time odometry and mapping on CPU without GPU acceleration
- Elimination of explicit LiDAR feature extraction and tracking
- Constant-time map updates independent of environment size
- Accuracy matching or exceeding state-of-the-art methods on aerial and ground datasets
Why it matters
Enables resource-constrained robotic platforms to perform accurate, real-time localization and environmental mapping simultaneously, benefiting autonomous navigation and collision avoidance.
Abstract
This letter 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 pro- posed Fast Truncated Distance Field (Fast-TDF) method as a con- venient tool to represent the environment, employing binary masks that encodes the L1 distance. 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.