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 systems are not available. Being visual, it relies on cameras, cheap, lightweight and versatile sensors, and being decentralized, it does not rely on communication to a central ground station. In this work, we integrate state-of-the-art decentralized SLAM components into a new, complete decentralized visual SLAM system. To allow for data association and co-optimization, existing decentralized visual SLAM systems regularly exchange the full map data between 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: in the first stage a compact full-image descriptor is deterministically sent to only one robot. In the second stage, which is only executed 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-the-art 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 data and we provide open access to the code.Comment: 8 pages, submitted to ICRA 201

Consistent Multi-robot Decentralized SLAM with Unknown Initial Positions

International audienceThis paper presents a multi-vehicle decentralized SLAM algorithm. We expose the different problems involved by this decentralized setting, such as network aspects (data losses, latencies or bandwidth requirements) or data incest (double-counting information), and address them. In order to ease the data association process and also guarantee the consistency of the vehicles localizations, we introduce a new model to represent the natural drift affecting SLAM algorithms. By integrating this model, loop closures, associations between robots and absolute information can be easily taken into account. A general framework has been designed thus allowing to use any SLAM algorithm. To demonstrate the feasibility of our approach, we applied it to a monocular solution. It is the first time, to our knowledge, that a monocular decentralized SLAM is presented. A multi-robot data association algorithm, based on geometric constraints is also exposed in this paper. The validation is performed thanks to a simulator presenting a realistic physics. The results show that the localizations consistency is preserved. It also demonstrates that multi-vehicle monocular SLAM is viable in urban environments