In this paper we describe a Simultaneous Localization and Mapping (SLAM) approach specifically designed to address the communication and computational issues that affect multi-robot systems. Our method utilizes condensed measurements to exchange map information between the robots. These measurements can effectively compress relevant portions of a map in a few data. This results in a substantial reduction of both the data to be transmitted and processed, that renders the system more robust and efficient. As documented by our simulated and real world experiments, these advantages come with a very little decrease in accuracy compared to ideal (but not realistic) methods that share the full data among all the robots.

As autonomous robots are increasingly being introduced in real-world environments operating for long periods of time, the difficulties of long-term mapping are attracting the attention of the robotics research community. This paper proposes a full SLAM system capable of handling the dynamics of the environment across a single or multiple mapping sessions. Using the pose graph SLAM paradigm, the system works on local maps in the form of 2D point cloud data which are updated over time to store the most up-to-date state of the environment. The core of our system is an efficient ICP-based alignment and merging procedure working on the clouds that copes with non-static entities of the environment. Furthermore, the system retains the graph complexity by removing out-dated nodes upon robust inter- and intra-session loop closure detections while graph coherency is preserved by using condensed measurements. Experiments conducted with real data from longterm SLAM datasets demonstrate the efficiency, accuracy and effectiveness of our system in the management of the mapping problem during long-term robot operation.

This paper reports an autonomous cooperative navigation system for robot formations in realistic scenarios. The formation movement control is based on a virtual structure composed by spring-dampers elements, which allows the formation to comply with the environment shape. A different navigation strategy is applied to the leader of the formation and to the rest of robots of the team. The leader plans the trajectories by using a two-level path planner with obstacle avoidance capabilities. The motion of the follower robots is controlled by the virtual structure, which adapts to the environment while the leader is tracked, taking into account the kinodynamic constraints of the vehicles. The system is evaluated in experiments carried out in simulations, some of them made in a realistic and complex urban scenario, and with real robots.

This paper presents an EKF-based approach to the problem of robot formation pose tracking in SLAM when a previously built feature-based stochastic map of a navigation area is available. We show how a direct implementation of the EKF algorithm leads to inconsistency in the estimated localization. We justify the origin of the anomalous behaviour of the filter in the time-correlated nature of the measurement noise sequence. A novel solution based on the measurement differencing technique is proposed to drive the solution of the EKF towards consistency. Both simulation and real experiments with a 3-robot triangular-shaped formation are reported.

Robot localization inside tunnels is a challenging task due to the special conditions of these environments. The GPS-denied nature of these scenarios, coupled with the low visibility, slippery and irregular surfaces, and lack of distinguishable visual and structural features, make traditional robotics methods based on cameras, lasers, or wheel encoders unreliable. Fortunately, tunnels provide other types of valuable information that can be used for localization purposes. On the one hand, radio frequency signal propagation in these types of scenarios shows a predictable periodic structure (periodic fadings) under certain settings, and on the other hand, tunnels present structural characteristics (e.g., galleries, emergency shelters) that must comply with safety regulations. The solution presented in this paper consists of detecting both types of features to be introduced as discrete sources of information in an alternative graph-based localization approach. The results obtained from experiments conducted in a real tunnel demonstrate the validity and suitability of the proposed system for inspection applications.