Versatile LiDAR-Inertial Odometry with SE(2) Constraints for Robust Ground Vehicle Localization

Temel Kavramlar

A hybrid LiDAR-inertial SLAM framework that leverages both onboard perception and prior motion dynamics to achieve robust and accurate localization for ground vehicles, by directly parameterizing poses on SE(2) while incorporating out-of-SE(2) perturbations.

Özet

The paper presents a versatile LiDAR-inertial SLAM framework for ground vehicles that leverages both onboard perception and prior motion dynamics to achieve robust and accurate localization.

Key highlights:

• Proposes a SE(2) constraints model that directly parameterizes ground vehicle poses while incorporating out-of-SE(2) perturbations as integrated noise, to handle real-world motion deviations from the SE(2) assumption.
• Develops a tightly coupled LiDAR-inertial odometry algorithm that provides real-time accurate state estimation with a high update rate.
• Evaluates the proposed method extensively on both outdoor (KITTI dataset) and indoor (warehouse) environments, demonstrating superior performance in accuracy and robustness compared to state-of-the-art LiDAR-based SLAM approaches.

The framework first performs IMU preintegration to obtain relative motion between LiDAR frames. It then extracts edge and planar features from the LiDAR scans and aligns them to the global map using the SE(2) constraints model. The out-of-SE(2) perturbations are incorporated into the integrated noise term of the constraints model to handle real-world motion deviations.

The tightly coupled LiDAR-inertial odometry algorithm fuses the LiDAR measurements and IMU data to provide accurate real-time state estimation. Extensive experiments show the proposed method outperforms existing LiDAR-only and loosely coupled LiDAR-inertial SLAM approaches in terms of localization accuracy and robustness, while maintaining real-time performance.

İstatistikler

The LiDAR sensor can obtain direct, dense, and accurate depth measurements of environments and is insusceptible from illumination and weather changes.
Ground vehicle motion in real-world environments often deviates from the SE(2) constraints model due to rough terrain or motion vibration.
LiDAR measurements are prone to bias, especially for high incidence angles when scanning distant surfaces, causing the trajectory to drift along the vertical direction.

Alıntılar

"LiDAR SLAM has become one of the major localization systems for ground vehicles since LiDAR Odometry And Mapping (LOAM)."
"Many extension works on LOAM mainly leverage one specific constraint to improve the performance, e.g., information from on-board sensors such as loop closure and inertial state; prior conditions such as ground level and motion dynamics."
"In many robotic applications, these conditions are often known partially, hence a SLAM system can be a comprehensive problem due to the existence of numerous constraints. Therefore, we can achieve a better SLAM result by fusing them properly."

Önemli Bilgiler Şuradan Elde Edildi

Versatile LiDAR-Inertial Odometry With SE (2) Constraints for Ground Vehicles

by Jiaying Chen... : arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01584.pdf

Daha Derin Sorular

How can the proposed SE(2) constraints model be extended to handle more complex motion patterns, such as those encountered in off-road or unstructured environments

The proposed SE(2) constraints model can be extended to handle more complex motion patterns encountered in off-road or unstructured environments by incorporating additional constraints and sensor modalities. In off-road scenarios, where the ground may not be flat or easily navigable, the model can be enhanced to include adaptive constraints that account for varying terrain elevations and obstacles. This can involve integrating data from additional sensors such as wheel encoders, cameras, or even terrain mapping sensors to provide a more comprehensive understanding of the environment. By fusing information from multiple sources, the system can adapt to challenging terrains and dynamically adjust the constraints to accommodate the vehicle's motion.

What are the potential limitations of the tightly coupled LiDAR-inertial odometry approach, and how could it be further improved to handle more challenging scenarios

The tightly coupled LiDAR-inertial odometry approach, while effective, may have limitations in handling extremely dynamic or unpredictable scenarios. One potential limitation is the reliance on accurate IMU measurements, which can be affected by sensor noise, calibration errors, or external disturbances. To address this, the system could be further improved by implementing robust sensor fusion techniques, such as sensor redundancy or sensor calibration algorithms, to enhance the accuracy and reliability of the IMU data. Additionally, integrating machine learning algorithms for sensor data processing and anomaly detection can help in identifying and mitigating errors in real-time, improving the system's robustness in challenging environments.

Given the versatility of the proposed framework, how could it be adapted to benefit other robotic applications beyond ground vehicles, such as aerial or underwater vehicles

The versatility of the proposed framework can be adapted to benefit other robotic applications beyond ground vehicles, such as aerial or underwater vehicles, by customizing the constraints and sensor fusion techniques to suit the specific dynamics of these platforms. For aerial vehicles, the framework can be modified to incorporate constraints that account for 3D motion dynamics, altitude changes, and aerodynamic factors. By integrating data from altimeters, GPS, and airspeed sensors, the system can provide accurate localization and mapping for drones or UAVs in various environments. Similarly, for underwater vehicles, the framework can be tailored to handle buoyancy, water currents, and depth variations by integrating data from pressure sensors, sonar systems, and inertial measurement units. This adaptation would enable precise localization and mapping capabilities for underwater exploration or marine applications.