A UKF-based Approach for Indoor Camera Trajectory Estimation

Abstract

When a camera moves in an unfamiliar environment, for many computer vision and robotic applications, it is desirable to estimate the camera position and orientation. Camera tracking is perhaps the most challenging part of the Visual Simultaneous Localization and Mapping (Visual SLAM) and Augmented Reality problems. This paper proposes a feature-based approach for tracking a hand-held camera that moves within an indoor place with a maximum depth of around 4-5 m. In the first few frames, the camera observes a chessboard as a marker to bootstrap the system and construct the initial map. Thereafter, upon the arrival of each new frame, the algorithm pursues the camera tracking procedure. This procedure is carried out in a framework that operates using only the extracted visible natural feature points and the initial map. The constructed initial map is extended as the camera explores new areas. In addition, the proposed system employs a hierarchical method on the basis of the Lucas-Kanade registration technique to track the FAST features. For each incoming frame, the 6-DOF camera pose parameters are estimated using an Unscented Kalman Filter (UKF). The proposed algorithm is tested on real-world videos, and the performance of UKF is compared against the other camera tracking methods. Two evaluation criteria (i.e. relative pose error and absolute trajectory error) are used in order to assess the performance of the proposed algorithm. Accordingly, the reported experimental results show the accuracy and effectiveness of the presented approach. The conducted experiments also indicate that the type of extracted feature points does not have a significant effect on the precision of the proposed approach.