Robust SRIF-based LiDAR-IMU Localization for Autonomous Vehicles

We present a tightly-coupled multi-sensor fusion architecture for autonomous vehicle applications, which achieves centimetre-level accuracy and high robustness in various scenarios. In order to realize robust and accurate point-cloud feature matching we propose a novel method for extracting structural, highly discriminative features from LiDAR point clouds. For high frequency motion prediction and noise propagation, we use incremental on-manifold IMU pre-integration. We also adopt a multi-frame sliding window square root inverse filter, so that the system maintains numerically stable results under the premise of limited power consumption. To verify our methodology, we test the fusion algorithm in multiple applications and platforms equipped with a LiDAR-IMU system. Our results demonstrate that our fusion framework attains state-of-the-art localization accuracy, high robustness and a good generalization ability.

Authors

• Kun Li
• Zhanpeng Ouyang
• Lan Hu
• Dayang Hao
• Laurent Kneip

Keywords

• Location awareness
• Laser radar
• Systematics
• Feature extraction
• Robot sensing systems
• Prediction algorithms
• Robustness
• Autonomous Vehicles
• Robust Localization
• Light Detection And Ranging
• Inertial Measurement Unit
• High Robustness
• Feature Matching
• Fusion Algorithm
• Good Generalization Ability
• Inverse Square Root
• Sliding Window
• Fusion Framework
• Point Cloud Features
• Global Positioning System
• Ground Plane
• Spatial Distance
• Global Map
• Global Navigation Satellite System
• Pose Estimation
• Linear Velocity
• Adjacent Points
• Corner Points
• Unmanned Ground Vehicles
• Line Features
• Motion Compensation
• Inertial Navigation
• Urban Canyon
• Odometry
• Ground Points
• Current Frame
• Environmental Scanning