Virtual Inverse Perspective Mapping for Simultaneous Pose and Motion Estimation

TL;DR

This paper introduces a novel virtual inverse perspective mapping technique for accurate, robust pose and motion estimation of ground-moving robots using a monocular camera, reducing cumulative errors common in visual odometry.

Contribution

It presents a semi-dense framework combining feature-based and image-registration methods with virtual IPM for improved accuracy in pose and motion estimation.

Findings

Relative mean error of pitch and roll angles ~1.0 degrees

Absolute mean error of travel distance ~0.3 mm

Effective under camera shaking and short-term disturbances

Abstract

We propose an automatic method for pose and motion estimation against a ground surface for a ground-moving robot-mounted monocular camera. The framework adopts a semi-dense approach that benefits from both a feature-based method and an image-registration-based method by setting multiple patches in the image for displacement computation through a highly accurate image-registration technique. To improve accuracy, we introduce virtual inverse perspective mapping (IPM) in the refinement step to eliminate the perspective effect on image registration. The pose and motion are jointly and robustly estimated by a formulation of geometric bundle adjustment via virtual IPM. Unlike conventional visual odometry methods, the proposed method is free from cumulative error because it directly estimates pose and motion against the ground by taking advantage of a camera configuration mounted on a ground-moving robot where the camera's vertical motion is ignorable compared to its height within the frame interval and the nearby ground surface is approximately flat. We conducted experiments in which the relative mean error of the pitch and roll angles was approximately 1.0 degrees and the absolute mean error of the travel distance was 0.3 mm, even under camera shaking within a short period.