A Stereo Visual Odometry Framework with Augmented Perception for Dynamic Urban Environments

One of main challenges of Visual Simultaneous Localization and Mapping (vSLAM) for navigation of mobile robots or automated driving systems is operating reliably in highly dynamic environments while assuming scene rigidity. In this paper, we propose a semantic-aware stereo visual odometry framework wherein feature extraction is performed over a static region-of-interest (ROI) generated through object detection and instance segmentation on a priori static street objects. This process generates a set of reliable features and a static map for pose estimation. Furthermore, the size of these quantities is downsized compared to a regular vSLAM map generation to reduce computational costs without compromising accuracy. A temporal window for pose estimation is also proposed to construct a covisibility graph and refine the pose estimation in a local bundle adjustment scheme; computational cost of the framework is also investigated in several driving scenarios. Extensive real driving sequences in various dynamic urban environments with varying sequence lengths confirms excellent performance and computational efficiency of the proposed bundle adjustment scheme using semantic-aware feature tracking compared to state-of-the-art approaches.