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3D SLAMWith Scan Matching and Factor Graph Optimization
For autonomous navigation, a mobile robot needs the capability to estimate its pose while simultaneously mapping its environment. This contribution presents an approach for fusing data from multiple asynchronous sensors using factor graphs. Full 3D SLAM is performed on data from several localization sensors and point clouds from a 3D LiDAR. The scans from the LiDAR are integrated by scan matching for relative motion estimation and are also used for loop closure.
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3D SLAMWith Scan Matching and Factor Graph Optimization
For autonomous navigation, a mobile robot needs the capability to estimate its pose while simultaneously mapping its environment. This contribution presents an approach for fusing data from multiple asynchronous sensors using factor graphs. Full 3D SLAM is performed on data from several localization sensors and point clouds from a 3D LiDAR. The scans from the LiDAR are integrated by scan matching for relative motion estimation and are also used for loop closure.
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https://ieeexplore.ieee.org/document/84706113D SLAMWith Scan Matching and Factor Graph Optimization
For autonomous navigation, a mobile robot needs the capability to estimate its pose while simultaneously mapping its environment. This contribution presents an approach for fusing data from multiple asynchronous sensors using factor graphs. Full 3D SLAM is performed on data from several localization sensors and point clouds from a 3D LiDAR. The scans from the LiDAR are integrated by scan matching for relative motion estimation and are also used for loop closure.
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