De-Skewing LiDAR Scan for Refinement of Local Mapping

Area of Science:

• Robotics
• Computer Vision
• Sensor Fusion

Background:

• Simultaneous Localization and Mapping (SLAM) is crucial for autonomous robots.
• High-acceleration motion causes LiDAR scan skewing, reducing mapping and classification accuracy.

Purpose of the Study:

• To improve mapping quality in autonomous robots by de-skewing LiDAR scans.
• To enhance the precision of Simultaneous Localization and Mapping (SLAM) systems.

Main Methods:

• Integrated high-sampling frequency Inertial Measurement Unit (IMU) data with LiDAR scans.
• Developed a motion equation to determine the pose of each point within a scan frame.
• Corrected and transformed scan points to a common frame, optimizing over local point cloud ranges.

Main Results:

• Successfully smoothed scan skewing across all channels.
• Demonstrated improved mapping accuracy in both indoor and outdoor experimental environments.
• Validated the algorithm's effectiveness in enhancing SLAM performance.

Conclusions:

• The proposed de-skewing LiDAR scan method significantly enhances mapping accuracy for autonomous robots.
• Integrating IMU data and optimizing over local point clouds are effective strategies for correcting motion artifacts.
• This technique offers a robust solution for precise robot localization and navigation.