Bundle Adjustment for Monocular Visual Odometry Based on Detected Traffic Sign Features

Abstract

Monocular visual odometry (VO), which is a subset of simultaneous localization and mapping (SLAM) used to determine the position and orientation of a moving object by analyzing the associated monocular camera image sequences, is a critical part in the vision system of autonomous driving. However, based on the frame-by-frame pose estimation, drift error can be incrementally accumulated. Bundle adjustment (BA) is thus introduced to deal with the error-drift problem through correlating several image frames together to optimize camera poses and extracted 3D map points simultaneously. In this paper, we propose a joint BA framework which takes into account additional constraints from the detected road traffic signs. This framework can be effectively integrated into existing VO systems, as evidenced by the improved vehicular localization accuracy in experimental performance when compared with the state-of-the-art baseline VO method.