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Most classical SLAM systems rely on the static scene assumption, which limitstheir applicability in real world scenarios. Recent SLAM frameworks have beenproposed to simultaneously track the camera and moving objects. However theyare often unable to estimate the canonical pose of the objects and exhibit alow object tracking accuracy. To solve this problem we propose TwistSLAM++, asemantic, dynamic, SLAM system that fuses stereo images and LiDAR information.Using semantic information, we track potentially moving objects and associatethem to 3D object detections in LiDAR scans to obtain their pose and size.Then, we perform registration on consecutive object scans to refine object poseestimation. Finally, object scans are used to estimate the shape of the objectand constrain map points to lie on the estimated surface within the BA. We showon classical benchmarks that this fusion approach based on multimodalinformation improves the accuracy of object tracking.

TwistSLAM++: Fusing multiple modalities for accurate dynamic semantic SLAM