Online Mapping from Weight Matching Odometry & Dynamic Point Cloud Filtering #Sciencefather #Researcherawards

Introduction

Efficient locomotion and autonomous navigation depend heavily on accurate mapping and clear perception of dynamic environments. The integration of LiDAR, IMU, and GNSS sensors leads to more robust odometry solutions, but challenges remain due to noise, environmental dynamics, and rapidly changing surroundings. This research addresses these problems by combining weight matching LiDAR-IMU-GNSS odometry with advanced point cloud filtering techniques based on pseudo-occupancy grids. By tackling both sensor fusion accuracy and object-level dynamic point removal, the proposed approach significantly improves online mapping quality and real-time reliability.

High-Accuracy Odometry through Weight Matching

The proposed odometry framework refines pose estimation by incorporating weight feature point matching, which leverages both geometric structures and reflectance intensity similarities. This dual-feature alignment provides superior robustness against environmental instability and sensor noise. IMU pre-integration ensures temporal consistency, while GNSS data contributes global positioning corrections. Together, these components help achieve higher precision compared to classical approaches and state-of-the-art systems such as LIO-SAM and FAST-LIO2.

Pseudo-Occupancy Grid for Dynamic Detection

A central innovation of the research is the introduction of a pseudo-occupancy grid structure that updates probability values based on occupancy ratios between current LiDAR frames and locally stored submaps. This mechanism enables the system to track and detect dynamic regions more effectively. Unlike traditional occupancy mapping models, which may struggle with rapidly moving objects, this pseudo-occupancy strategy quickly identifies unstable areas and supports real-time filtering decisions.

Object-Level Dynamic Point Cloud Filtering

Dynamic objects such as vehicles, cyclists, and pedestrians introduce significant motion-induced artifacts that reduce mapping consistency. To overcome this, the method applies curved voxel clustering to segment object-level clusters from the point cloud. The dynamic components are then filtered out, resulting in cleaner static maps suitable for navigation and localization tasks. This step improves clarity, reduces computational load in later stages, and enhances overall system safety.

Comparative Performance Evaluation

Extensive experiments conducted on datasets such as KITTI, UrbanLoco, and the Newer College Dataset validate the efficiency and accuracy of the proposed methodology. Compared with established frameworks like LIO-SAM and FAST-LIO2, the system demonstrates superior localization and mapping performance across urban, semi-urban, and campus environments. Additionally, dynamic filtering performance surpasses that of Removert and ERASOR, confirming the robustness of the pseudo-occupancy and voxel clustering approach.

Advancements in Real-Time Autonomous Mapping

The proposed solution not only enhances mapping accuracy but also improves real-time performance and adaptability in highly dynamic scenes. Its ability to simultaneously process odometry, dynamic filtering, segmentation, and submap alignment makes it well-suited for modern autonomous robots and driverless vehicles. As the system maintains high accuracy even in challenging scenarios, it contributes significantly to next-generation autonomous navigation research.


 Global Particle Physics Excellence Awards


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Hashtags

#Sciencefather, #Reseacherawards, #MappingResearch, #LiDARIMUGNSS, #DynamicFiltering, #AutonomousDriving, #OnlineMapping, #PointCloudProcessing, #OdometryResearch, #RobotNavigation, #PseudoOccupancyGrid, #VoxelClustering, #SensorFusion, #RealTimeMapping, #HighAccuracyMapping, #KITTI, #UrbanLoco, #NCD, #SLAMResearch, #RoboticsInnovation, #DynamicObjectRemoval, #AdvancedMapping,

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