Range-only Collaborative Localization for Ground Vehicles

High-accuracy absolute localization for a team of vehicles is essential when accomplishing various kinds of tasks. As a promising approach, collaborative localization fuses the individual motion measurements and the inter-vehicle measurements to collaboratively estimate the states. In this paper, we focus on the range-only collaborative localization, which specifies the inter-vehicle measurements as inter-vehicle ranging measurements. We first investigate the observability properties of the system and derive that to achieve bounded localization errors, two vehicles are required to remain static like external infrastructures. Under the guide of the observability analysis, we then propose our range-only collaborative localization system which categorize the ground vehicles into two static vehicles and dynamic vehicles. The vehicles are connected utilizing a UWB network that is capable of both producing inter-vehicle ranging measurements and communication. Simulation results validate the observability analysis and demonstrate that collaborative localization is capable of achieving higher accuracy when utilizing the inter-vehicle measurements. Extensive experimental results are performed for a team of 3 and 5 vehicles. The real-world results illustrate that our proposed system enables accurate and real-time estimation of all vehicles' absolute poses.Comment: Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019

Lidar-Based Relative Position Estimation and Tracking for Multi-Robot Systems

Relative positioning systems play a vital role in current multi-robot systems. We present a self-contained detection and tracking approach, where a robot estimates a distance (range) and an angle (bearing) to another robot using measurements extracted from the raw data provided by two laser range finders. We propose a method based on the detection of circular features with least-squares fitting and filtering out outliers using a map-based selection. We improve the estimate of the relative robot position and reduce its uncertainty by feeding measurements into a Kalman filter, resulting in an accurate tracking system. We evaluate the performance of the algorithm in a realistic indoor environment to demonstrate its robustness and reliability

Cooperative localization of drones by using interval methods

In this article we address the problem of cooperative pose estimation in a group of unmanned aerial vehicles (UAVs) in a bounded-error context. The UAVs are equipped with cameras to track landmarks, and with a communication and ranging system to cooperate with their neighbors. Measurements are represented by intervals, and constraints are expressed on the robots poses (positions and orientations). Pose domains subpavings are obtained by using set inversion via interval analysis. Each robot of the group first computes a pose domain using only its sensors measurements. Then, through position boxes exchanges, the positions are cooperatively refined by constraint propagation in the group. Results with real robot data are presented, and show that the position accuracy is improved thanks to cooperation

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