Longitudinal error improvement by visual odometry trajectory trail and road segment matching
As one of the key requirements in the intelligent vehicle, accurate and precise localisation is essential to ensure swift route planning during the drive. In this study, the authors would like to reduce the longitudinal positioning error that remains as a challenge in accurate localisation. To solve this, they propose a data fusion method by integrating information from visual odometry (VO), noisy GPS, and road information obtained from the publicly available digital map with particle filter. The curve of the VO trajectory trail is compared with road segments curve to increase longitudinal accuracy. This method is validated by KITTI dataset, tested with different GPS noise conditions, and the results show improved localisation for both lateral and longitudinal positioning errors.
Over the course of the last two decades, continuous advances in the stereo vision field have been documented. In this paper we present an analysis of the efficiency for the stereo vision algorithm of the Census Transform algorithm. In addition to the conventional correlation method based on Hamming distance minimization, we use two similarity measures: the Tanimoto and the Dixon-Koehler distances. Then, we compare its performance in terms of accuracy and hardware resources needed for implementation. These comparisons are performed by introducing a generalized model for each hardware architecture, scalable depending on design parameters such as Census Transform window size and maximum disparity range.