SimVODIS++: Neural Semantic Visual Odometry in Dynamic Environments
Ue-Hwan Kim, Se-Ho Kim, Jong-Hwan Kim
Abstract
Accurate estimation of 3D geometry and camera motion enables a wide range of tasks in robotics and autonomous vehicles. However, the lack of semantics and the performance degradation due to dynamic objects hinder its application to real-world scenarios. To overcome these limitations, we design a novel neural semantic visual odometry (VO) architecture on top of the simultaneous VO, object detection and instance segmentation (SimVODIS) network. Next, we propose an attentive pose estimation architecture with a multi-task learning formulation for handling dynamic objects and VO performance enhancement. The extensive experiments conducted in our work attest that the proposed SimVODIS++ improves the VO performance in dynamic environments. Further, SimVODIS++ focuses on salient regions while excluding feature-less regions. Performing the experiments, we have discovered and fixed the data leakage problem in the conventional experiment setting followed by numerous previous works—which we claim as one of our contributions. We make the source code public.