  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.
