Accurate and Robust Scale Recovery for Monocular Visual Odometry Based
on Plane Geometry
- URL: http://arxiv.org/abs/2101.05995v1
- Date: Fri, 15 Jan 2021 07:21:24 GMT
- Title: Accurate and Robust Scale Recovery for Monocular Visual Odometry Based
on Plane Geometry
- Authors: Rui Tian, Yunzhou Zhang, Delong Zhu, Shiwen Liang, Sonya Coleman,
Dermot Kerr
- Abstract summary: We develop a lightweight scale recovery framework leveraging an accurate and robust estimation of the ground plane.
Experiments on the KITTI dataset show that the proposed framework can achieve state-of-theart accuracy in terms of translation errors.
Due to the light-weight design, our framework also demonstrates a high frequency of 20Hz on the dataset.
- Score: 7.169216737580712
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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