论文标题
Twistslam ++：融合多种方式以进行准确的动态语义大满贯
TwistSLAM++: Fusing multiple modalities for accurate dynamic semantic SLAM
论文作者
论文摘要
大多数经典的大满贯系统都依赖于静态场景假设，该假设限制了其在现实世界中的适用性。最近提出了最近的SLAM框架来同时跟踪相机和移动的对象。但是，他们通常无法估计物体的规范姿势并表现出低对象跟踪精度。为了解决这个问题，我们提出了Twistslam ++，这是一种语义，动态的大满贯系统，它融合了立体声图像和LiDAR信息。使用语义信息，我们跟踪可能移动对象，并将它们与LIDAR扫描中的3D对象检测相关联，以获得其姿势和大小。然后，我们对连续对象扫描进行注册以完善对象姿势估计。最后，使用对象扫描来估计对象的形状，并约束MAP点位于BA内的估计表面上。我们在经典的基准上表明，基于多模式信息的这种融合方法提高了对象跟踪的准确性。
Most classical SLAM systems rely on the static scene assumption, which limits their applicability in real world scenarios. Recent SLAM frameworks have been proposed to simultaneously track the camera and moving objects. However they are often unable to estimate the canonical pose of the objects and exhibit a low object tracking accuracy. To solve this problem we propose TwistSLAM++, a semantic, dynamic, SLAM system that fuses stereo images and LiDAR information. Using semantic information, we track potentially moving objects and associate them to 3D object detections in LiDAR scans to obtain their pose and size. Then, we perform registration on consecutive object scans to refine object pose estimation. Finally, object scans are used to estimate the shape of the object and constrain map points to lie on the estimated surface within the BA. We show on classical benchmarks that this fusion approach based on multimodal information improves the accuracy of object tracking.
