Year : 2018 Data-Efficient Decentralized Visual SLAM

Decentralized visual simultaneous localization and mapping (SLAM) is a powerful tool for multi-robot applications in environments where absolute positioning is not available. Being visual, it relies on cheap, lightweight and versatile cameras, and, being decentralized, it does not rely on communication to a central entity. In this work, we integrate state-of-theart decentralized SLAM components into a new, complete decentralized visual SLAM system. To allow for data association and optimization, existing decentralized visual SLAM systems exchange the full map data among all robots, incurring large data transfers at a complexity that scales quadratically with the robot count. In contrast, our method performs efficient data association in two stages: first, a compact full-image descriptor is deterministically sent to only one robot. Then, only if the first stage succeeded, the data required for relative pose estimation is sent, again to only one robot. Thus, data association scales linearly with the robot count and uses highly compact place representations. For optimization, a state-of-theart decentralized pose-graph optimization method is used. It exchanges a minimum amount of data which is linear with trajectory overlap. We characterize the resulting system and identify bottlenecks in its components. The system is evaluated on publicly available datasets and we provide open access to the code. A narrated video presentation can be found at: https://youtu.be/zEBfCA5tVOk Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-150442 Published Version Originally published at: Cieslewski, Titus; Choudhary, Siddharth; Scaramuzza, Davide (2018). Data-Efficient Decentralized Visual SLAM. In: IEEE International Conference on Robotics and Automation (ICRA), 2018. , Brisbane, 1 May 2018 25 May 2018, 1-8. Data-Efficient Decentralized Visual SLAM Titus Cieslewski1, Siddharth Choudhary2 and Davide Scaramuzza1 Abstract— Decentralized visual simultaneous localization and mapping (SLAM) is a powerful tool for multi-robot applications in environments where absolute positioning is not available. Being visual, it relies on cheap, lightweight and versatile cameras, and, being decentralized, it does not rely on communication to a central entity. In this work, we integrate state-of-theart decentralized SLAM components into a new, complete decentralized visual SLAM system. To allow for data association and optimization, existing decentralized visual SLAM systems exchange the full map data among all robots, incurring large data transfers at a complexity that scales quadratically with the robot count. In contrast, our method performs efficient data association in two stages: first, a compact full-image descriptor is deterministically sent to only one robot. Then, only if the first stage succeeded, the data required for relative pose estimation is sent, again to only one robot. Thus, data association scales linearly with the robot count and uses highly compact place representations. For optimization, a state-of-theart decentralized pose-graph optimization method is used. It exchanges a minimum amount of data which is linear with trajectory overlap. We characterize the resulting system and identify bottlenecks in its components. The system is evaluated on publicly available datasets and we provide open access to the code. Decentralized visual simultaneous localization and mapping (SLAM) is a powerful tool for multi-robot applications in environments where absolute positioning is not available. Being visual, it relies on cheap, lightweight and versatile cameras, and, being decentralized, it does not rely on communication to a central entity. In this work, we integrate state-of-theart decentralized SLAM components into a new, complete decentralized visual SLAM system. To allow for data association and optimization, existing decentralized visual SLAM systems exchange the full map data among all robots, incurring large data transfers at a complexity that scales quadratically with the robot count. In contrast, our method performs efficient data association in two stages: first, a compact full-image descriptor is deterministically sent to only one robot. Then, only if the first stage succeeded, the data required for relative pose estimation is sent, again to only one robot. Thus, data association scales linearly with the robot count and uses highly compact place representations. For optimization, a state-of-theart decentralized pose-graph optimization method is used. It exchanges a minimum amount of data which is linear with trajectory overlap. We characterize the resulting system and identify bottlenecks in its components. The system is evaluated on publicly available datasets and we provide open access to the code. SUPPLEMENTARY MATERIAL Data and code are at: https://github.com/