Robust Realtime RGBD Visual Odometry in Dynamic Environments via Rigid Motion Model In the paper, we propose a robust realtime visual odometry in dynamic environments via rigidmotion model updated by scene flow. The proposed algorithm consists of spatial motion segmentation and temporal motion tracking. The spatial segmentation first generates several motion hypotheses by using a gridbased scene flow and clusters the extracted motion hypotheses, separating objects that move independently of one another. Further, we use a dualmode motion model to consistently distinguish between the static and dynamic parts in the temporal motion tracking stage. Finally, the proposed algorithm estimates the pose of a camera by taking advantage of the region classified as static parts. In order to evaluate the performance of visual odometry under the existence of dynamic rigid objects, we use selfcollected dataset containing RGBD images and motion capture data for groundtruth. We compare our algorithm with stateoftheart visual odometry algorithms. The validation results suggest that the proposed algorithm can estimate the pose of a camera robustly and accurately in dynamic environments.

ReFusion: 3D Reconstruction in Dynamic Environments for RGBD Cameras Exploiting Residuals Mapping and localization are essential capabilities of robotic systems. Although the majority of mapping systems focus on static environments, the deployment in realworld situations requires them to handle dynamic objects. In this paper, we propose an approach for an RGBD sensor that is able to consistently map scenes containing multiple dynamic elements. For localization and mapping, we employ an efficient direct tracking on the truncated signed distance function (TSDF) and leverage color information encoded in the TSDF to estimate the pose of the sensor. The TSDF is efficiently represented using voxel hashing, with most computations parallelized on a GPU. For detecting dynamics, we exploit the residuals obtained after an initial registration, together with the explicit modeling of free space in the model. We evaluate our approach on existing datasets, and provide a new dataset showing highly dynamic scenes. These experiments show that our approach often surpass other stateoftheart dense SLAM methods. We make available our dataset with the ground truth for both the trajectory of the RGBD sensor obtained by a motion capture system and the model of the static environment using a highprecision terrestrial laser scanner. Finally, we release our approach as open source code.