Wu, W.; Wang, W. LiDAR Inertial Odometry Based on Indexed Point and Delayed Removal Strategy in Highly Dynamic Environments. Sensors2023, 23, 5188.
Wu, W.; Wang, W. LiDAR Inertial Odometry Based on Indexed Point and Delayed Removal Strategy in Highly Dynamic Environments. Sensors 2023, 23, 5188.
Wu, W.; Wang, W. LiDAR Inertial Odometry Based on Indexed Point and Delayed Removal Strategy in Highly Dynamic Environments. Sensors2023, 23, 5188.
Wu, W.; Wang, W. LiDAR Inertial Odometry Based on Indexed Point and Delayed Removal Strategy in Highly Dynamic Environments. Sensors 2023, 23, 5188.
Abstract
Simultaneous Localization and Mapping (SLAM) is considered as a challenge in environments with many moving objects. This paper proposes a novel LiDAR Inertial Odometry framework, ID-LIO, for dynamic scenes that builds on LIO-SAM. To detect the pointclouds on the moving objects, a dynamic point detection method is integrated, which is based on pseudo occupancy along the spatial dimension. Then, we present a dynamic points propagation and removal algorithm based on indexed points to remove more dynamic points in the local map along the temporal dimension and update the status of the point features in keyframes. In the LiDAR odometry module, a delay removal strategy is proposed for history keyframes and the sliding window-based optimization includes the LIDAR measurement with dynamic weights to reduce error from dynamic points in keyframes. We perform the experiments both on the public low dynamic and highly dynamic data set. The results show that the proposed method greatly increases the localization accuracy in high dynamic environments. And the ATE(Absolute Trajectory Error) average RMSE((Root Mean Square Error) of our ID-LIO can be improved by 67% and 85% in the UrbanLoco-CAMarketStreet dataset and UrbanNav-HK-Medium-Urban-1 dataset respectively when compared with LIO-SAM.
Copyright:
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