An adaptive observer framework for accurate feature depth estimation using an uncalibrated monocular camera

• A globally exponentially stable observer for depth estimation with an uncalibrated camera is developed.
• Experimental results demonstrate the superiority of the proposed observer over the EKF.
• Motion sequences can be processed without prior knowledge of the camera and scene geometry.

This paper presents a novel solution to the problem of depth estimation using a monocular camera undergoing known motion. Such problems arise in machine vision where the position of an object moving in three-dimensional space has to be identified by tracking motion of its projected feature on the two-dimensional image plane. The camera is assumed to be uncalibrated, and an adaptive observer yielding asymptotic estimates of focal length and feature depth is developed that precludes prior knowledge of scene geometry and is simpler than alternative designs. Experimental results using real camera imagery are obtained with the current scheme as well as the extended Kalman filter, and performance of the proposed observer is shown to be better than the extended Kalman filter-based framework.