Localization in a dynamic environment suffers from moving objects. Removing dynamic object is crucial in this situation but become tricky when ego-motion is coupled. In this paper, instead of proposing a new slam framework, we aim at a more general strategy for a localization scenario. In that case, Dynamic Registration is available for integrating with any lidar slam system. We utilize 3D object detection to obtain potential moving objects and remove them temporarily. Then we proposed Dynamic Registration, to iteratively estimate ego-motion and segment moving objects until no static object generates. Static objects are merged with the environment. Finally, we successfully segment dynamic objects, static environments with static objects, and ego-motion estimation in a dynamic environment. We evaluate the performance of our proposed method on KITTI Tracking datasets. Results show stable and consistent improvements based on other classical registration algorithms.
翻译:动态环境中的本地化会受到移动对象的影响。 在这种情形下, 移除动态对象是关键, 但当自我感动同时出现时会变得很棘手。 在本文中, 我们不是提出一个新的自我感动框架, 而是要为定位设想制定更宽泛的战略。 在这种情况下, 动态登记可以与任何 lidar slam 系统整合。 我们使用 3D 对象探测来获取潜在的移动对象并暂时删除它们。 然后我们提议动态登记, 以迭接方式估计自我感动和部分感动对象, 直到没有生成静态对象。 静态对象会与环境合并。 最后, 我们成功地将动态物体、 静态对象的静态环境以及动态环境中的自我感动估计分解。 我们评估了我们关于 KITTI 跟踪数据集的拟议方法的性能。 结果显示基于其他传统注册算法的稳定和一致的改进 。