ECMR’13 Special Issue

This special issue contains extended versions of the best papers from the 6th European Conference on Mobile Robots (ECMR). ECMR is a biennial European forum, internationally open, that allows roboticists throughout Europe to become acquainted with the latest research accomplishments and innovations in mobile robotics and mobile human–robot systems. ECMR covers most aspects of mobile robotics research and machine intelligence, including (but not limited to) the following topics: multi-sensor fusion, localization, map building, navigation, active perception, behavior-based robotics, path and task planning, learning and adaptation, robot vision, human–robot interaction, cognitive robotics, experimental evaluation and benchmarking, 3D sensing, and applications of mobile robotics in land, water, air, underground, and space.Peer ReviewedPostprint (author's final draft

Editorial: special issue on autonomous driving and driver assistance systems

No abstract availablepublishe

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Cooperative localization for mobile agents: a recursive decentralized algorithm based on Kalman filter decoupling

We consider cooperative localization technique for mobile agents with communication and computation capabilities. We start by provide and overview of different decentralization strategies in the literature, with special focus on how these algorithms maintain an account of intrinsic correlations between state estimate of team members. Then, we present a novel decentralized cooperative localization algorithm that is a decentralized implementation of a centralized Extended Kalman Filter for cooperative localization. In this algorithm, instead of propagating cross-covariance terms, each agent propagates new intermediate local variables that can be used in an update stage to create the required propagated cross-covariance terms. Whenever there is a relative measurement in the network, the algorithm declares the agent making this measurement as the interim master. By acquiring information from the interim landmark, the agent the relative measurement is taken from, the interim master can calculate and broadcast a set of intermediate variables which each robot can then use to update its estimates to match that of a centralized Extended Kalman Filter for cooperative localization. Once an update is done, no further communication is needed until the next relative measurement

Fast, on-board, model-aided visual-inertial odometry system for quadrotor micro aerial vehicles

© 2016 IEEE. The main contribution of this paper is a high frequency, low-complexity, on-board visual-inertial odometry system for quadrotor micro air vehicles. The system consists of an extended Kalman filter (EKF) based state estimation algorithm that fuses information from a low cost MEMS inertial measurement unit acquired at 200Hz and VGA resolution images from a monocular camera at 50Hz. The dynamic model describing the quadrotor motion is employed in the estimation algorithm as a third source of information. Visual information is incorporated into the EKF by enforcing the epipolar constraint on features tracked between image pairs, avoiding the need to explicitly estimate the location of the tracked environmental features. Combined use of the dynamic model and epipolar constraints makes it possible to obtain drift free velocity and attitude estimates in the presence of both accelerometer and gyroscope biases. A strategy to deal with the unobservability that arises when the quadrotor is in hover is also provided. Experimental data from a real-time implementation of the system on a 50 gram embedded computer are presented in addition to the simulations to demonstrate the efficacy of the proposed system

3D Multi-Robot Exploration with a Two-Level Coordination Strategy and Prioritization

This work presents a 3D multi-robot exploration framework for a team of UGVs moving on uneven terrains. The framework was designed by casting the two-level coordination strategy presented in [1] into the context of multi-robot exploration. The resulting distributed exploration technique minimizes and explicitly manages the occurrence of conflicts and interferences in the robot team. Each robot selects where to scan next by using a receding horizon next-best-view approach [2]. A sampling-based tree is directly expanded on segmented traversable regions of the terrain 3D map to generate the candidate next viewpoints. During the exploration, users can assign locations with higher priorities on-demand to steer the robot exploration toward areas of interest. The proposed framework can be also used to perform coverage tasks in the case a map of the environment is a priori provided as input. An open-source implementation is available online