LiDAR Odometry Survey: Recent Advancements and Remaining Challenges

TL;DR

This survey addresses the challenge of robust odometry in GPS-denied settings by surveying advancements in LiDAR-based odometry across pure LiDAR, LiDAR-inertial fusion, and multi-sensor fusion. It organizes methods into LiDAR-only, LiDAR-inertial, multi-LiDAR, and cross-modal fusion categories, and analyzes public datasets and evaluation protocols to enable fair comparisons. Key contributions include a structured taxonomy, critical discussion of remaining challenges (data volume, de-skewing, sensor heterogeneity, and adverse environments), and guidance on selecting appropriate fusion strategies for different operation regimes. The work underscores the practical significance of LiDAR odometry for autonomous navigation and points to future directions that leverage deep learning and richer multi-modal sensing to achieve robust, real-time performance.

Abstract

Odometry is crucial for robot navigation, particularly in situations where global positioning methods like global positioning system (GPS) are unavailable. The main goal of odometry is to predict the robot's motion and accurately determine its current location. Various sensors, such as wheel encoder, inertial measurement unit (IMU), camera, radar, and Light Detection and Ranging (LiDAR), are used for odometry in robotics. LiDAR, in particular, has gained attention for its ability to provide rich three-dimensional (3D) data and immunity to light variations. This survey aims to examine advancements in LiDAR odometry thoroughly. We start by exploring LiDAR technology and then scrutinize LiDAR odometry works, categorizing them based on their sensor integration approaches. These approaches include methods relying solely on LiDAR, those combining LiDAR with IMU, strategies involving multiple LiDARs, and methods fusing LiDAR with other sensor modalities. In conclusion, we address existing challenges and outline potential future directions in LiDAR odometry. Additionally, we analyze public datasets and evaluation methods for LiDAR odometry. To our knowledge, this survey is the first comprehensive exploration of LiDAR odometry.

Figures (5)

• Figure 1: Structure of LiDAR Odometry Survey. Section §\ref{['sec2']} explores the intricacies of LiDAR technology. Section §\ref{['sec3']} -- §\ref{['sec6']} investigate the LiDAR odometry under different sensor modalities. Section §\ref{['sec7']} introduces the ongoing challenges in LiDAR odometry. Finally, Section §\ref{['sec8']} presents the public datasets, evaluation metrics, and benchmark results.
• Figure 2: Diverse LiDAR Scanning Patterns. The figure displays repetitive and non-repetitive patterns captured from real LiDAR sensors. In (a), Velodyne VLP-16, a mechanical LiDAR, shows a vertical channel-based repetitive pattern. In (b), Livox Mid-70, a scanning solid-state LiDAR with Risely prisms, displays a unique lotus-shaped, non-repetitive pattern.
• Figure 3: LiDAR Odometry Pipeline. The common framework for LiDAR odometry can be broadly divided into three stages: pre-processing, initial estimation, and state estimation. When incorporating other sensors, their integration is classified as either loosely-coupled or tightly-coupled, based on the specific stage at which the additional sensor data is utilized. In the state estimation stage, the refined state is leveraged both in the odometry and mapping.
• Figure 4: Various Evaluation Methods of LiDAR Odometry. This figure illustrates diverse assessment methods for LiDAR odometry. (a) Trajectory Error shows local and global discrepancies along the estimated path. (b) Start-to-End Error highlights long-term drifts from start to finish. (c) GCP-based Error assesses alignment with GCPs for real-world accuracy. (d) Entropy-based Error reflects scan registration quality and overall system reliability.
• Figure 5: Estimated Trajectories. This figure illustrates results from the HeLiPR and ConSLAM datasets. In Figure (b), the red box zooms in on instances where the LiDAR odometry deviates from the ground truth compared to LiDAR-inertial odometry.