Scale ambiguity is a fundamental problem in monocular visual odometry. Typical solutions include loop closure detection and environment information mining. For applications like self-driving cars, loop closure is not always available, hence mining prior knowledge from the environment becomes a more promising approach. In this paper, with the assumption of a constant height of the camera above the ground, we develop a light-weight scale recovery framework leveraging an accurate and robust estimation of the ground plane. The framework includes a ground point extraction algorithm for selecting high-quality points on the ground plane, and a ground point aggregation algorithm for joining the extracted ground points in a local sliding window. Based on the aggregated data, the scale is finally recovered by solving a least-squares problem using a RANSAC-based optimizer. Sufficient data and robust optimizer enable a highly accurate scale recovery. Experiments on the KITTI dataset show that the proposed framework can achieve state-of-the-art accuracy in terms of translation errors, while maintaining competitive performance on the rotation error. Due to the light-weight design, our framework also demonstrates a high frequency of 20Hz on the dataset.
|Published (in print/issue) - 30 May 2021
|2021 IEEE International Conference on Robotics and Automation (ICRA): ICRA 21 - Xian, China, China
Duration: 30 May 2021 → 5 Jun 2021
|2021 IEEE International Conference on Robotics and Automation (ICRA)
|30/05/21 → 5/06/21
- Monocular Visual Odometry
- Scale ambiguity