Sliding Windowed Optimization Algorithm for Fusion of Redundant Stereo Visual Odometries
ResumoWe propose a sliding windowed graph-optimization based approach for the fusion of two stereo visual odometries with 6 degrees of freedom poses. Stereo odometries are calculated independently, using pairs of stereo images that are captured by stereo cameras mounted on the top of a moving platform. We implemented our sliding windowed approach using the public library LIBVISO2. Our results are compared against the two recognized SLAM frameworks ORB-SLAM2 and UCOSLAM. The relative pose error of the fused poses decreases by up to 94% in relation to the error of single stereo odometry and by up to 91% compared with the results of UCOSLAM.
Palavras-chave: Visualization, Simultaneous localization and mapping, Motion estimation, Robot vision systems, Redundancy, Cameras, Libraries, Sensor fusion, Stereo Visual Odometry, Sliding Windowed Optimization, Errors Accuracy
CABRERA-AVILA, Elizabeth V.; SILVA, Bruno M. F.; GONÇALVES, Luiz M. G.. Sliding Windowed Optimization Algorithm for Fusion of Redundant Stereo Visual Odometries. In: SIMPÓSIO BRASILEIRO DE ROBÓTICA E SIMPÓSIO LATINO AMERICANO DE ROBÓTICA (SBR/LARS), 19. , 2022, São Bernardo do Campo/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 300-305.