Robust Lidar-Based Localization Scheme for Unmanned Ground Vehicle via Multisensor Fusion

Area of Science:

• Robotics
• Sensor Fusion
• Simultaneous Localization and Mapping (SLAM)

Background:

• Unmanned Ground Vehicles (UGVs) require precise localization for autonomous operation.
• Global Positioning System (GPS) is unreliable in GPS-denied or challenged environments.
• Multisensor fusion offers a robust solution for accurate UGV localization.

Purpose of the Study:

• To propose a robust and precise localization scheme for UGVs.
• To enable UGV navigation in GPS-denied and GPS-challenged environments.
• To leverage multisensor fusion with point-cloud maps for enhanced localization.

Main Methods:

• Utilizing a multisensor fusion approach combining 3D lidar and Inertial Measurement Unit (IMU) data.
• Implementing a Gaussian projection approach for initial UGV pose estimation.
• Employing scan-to-submap alignment for accurate pose calculation relative to a point-cloud map.
• Integrating 3D lidar and IMU data for precise pose prediction.

Main Results:

• Achieved robust and precise localization for UGVs in challenging environments.
• Demonstrated effective frame transformation between 3D lidar and IMU.
• Successfully predicted UGV pose by fusing sensor data.
• Validated the proposed localization scheme through campus experiments.

Conclusions:

• The proposed multisensor fusion localization scheme provides a reliable solution for UGVs in GPS-limited conditions.
• The integration of 3D lidar, IMU, and point-cloud maps significantly enhances localization accuracy.
• The developed visualization module aids in verifying the system's performance.