A flexible framework for accurate LiDAR odometry, map manipulation, and localization
arXiv:2407.20465 · doi:10.1177/02783649251316881
Abstract
LiDAR-based SLAM is a core technology for autonomous vehicles and robots. One key contribution of this work to 3D LiDAR SLAM and localization is a fierce defense of view-based maps (pose graphs with time-stamped sensor readings) as the fundamental representation of maps. As will be shown, they allow for the greatest flexibility, enabling the posterior generation of arbitrary metric maps optimized for particular tasks, e.g. obstacle avoidance, real-time localization. Moreover, this work introduces a new framework in which mapping pipelines can be defined without coding, defining the connections of a network of reusable blocks much like deep-learning networks are designed by connecting layers of standardized elements. We also introduce tightly-coupled estimation of linear and angular velocity vectors within the Iterative Closest Point (ICP)-like optimizer, leading to superior robustness against aggressive motion profiles without the need for an IMU. Extensive experimental validation reveals that the proposal compares well to, or improves, former state-of-the-art (SOTA) LiDAR odometry systems, while also successfully mapping some hard sequences where others diverge. A proposed self-adaptive configuration has been used, without parameter changes, for all 3D LiDAR datasets with sensors between 16 and 128 rings, and has been extensively tested on 83 sequences over more than 250~km of automotive, hand-held, airborne, and quadruped LiDAR datasets, both indoors and outdoors. The system flexibility is demonstrated with additional configurations for 2D LiDARs and for building 3D NDT-like maps. The framework is open-sourced online: https://github.com/MOLAorg/mola
44 pages, 35 figures
References in corpus (19)
- ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM
- Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
- On-Manifold Preintegration for Real-Time Visual-Inertial Odometry
- KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Way
- SuMa++: Efficient LiDAR-based Semantic SLAM
- Online Global Loop Closure Detection for Large-Scale Multi-Session Graph-Based SLAM
- ERASOR: Egocentric Ratio of Pseudo Occupancy-based Dynamic Object Removal for Static 3D Point Cloud Map Building
- The Newer College Dataset: Handheld LiDAR, Inertial and Vision with Ground Truth
- NTU VIRAL: A Visual-Inertial-Ranging-Lidar Dataset, From an Aerial Vehicle Viewpoint
- The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection
- The Hilti SLAM Challenge Dataset
- LOCUS 2.0: Robust and Computationally Efficient Lidar Odometry for Real-Time Underground 3D Mapping
- Voxgraph: Globally Consistent, Volumetric Mapping using Signed Distance Function Submaps
- MILIOM: Tightly Coupled Multi-Input Lidar-Inertia Odometry and Mapping
- COIN-LIO: Complementary Intensity-Augmented LiDAR Inertial Odometry
- Eigen Is All You Need: Efficient Lidar-Inertial Continuous-Time Odometry with Internal Association
- Multi S-Graphs: An Efficient Distributed Semantic-Relational Collaborative SLAM
- Benchmarking Particle Filter Algorithms for Efficient Velodyne-Based Vehicle Localization
- One Ring to Rule Them All: Certifiably Robust Geometric Perception with Outliers