Cooperative Relative Localization Using Range Measurements Without a Priori Information

Abstract

The ability for autonomous vehicles to cooperatively navigate, especially in GPS denied environments, is becoming increasingly important. It also requires the ability to initialize, or reinitialize estimation algorithms for cooperative systems on-the-fly in cases where precise a priori state information is unavailable. In this paper, we provide a framework that allows estimation of the relative pose and orientation between vehicles in the presence of high initial uncertainty. Effects of cooperation among multiple vehicles exchanging estimates of heading rate and velocity and external sensor measurements are analyzed. A Multi-Hypothesis Extended Kalman Filter (MHEKF) technique is used to initialize pairwise vehicles using range-only measurements. Using solutions identified by the MHEKF algorithm, a joint filter comprising of multiple vehicles is initialized. Sufficient conditions to maintain bounded errors are derived through nonlinear observability analysis using Lie derivatives for the pairwise and the multi-vehicle cases. Using these conditions as passive constraints in the system, simulation and hardware experiments are performed to demonstrate the advantages of using MHEKF when the initial conditions are unreliable. A multi-vehicle testbed for heterogeneous platforms with different sensing modalities is developed to facilitate hardware testing. Improvements in the system performance when cooperation is introduced among vehicles is also highlighted through experiments.

Two‐stage approach for cooperative multi‐vehicle localization using integrated measurements

“…They showed that their algorithm converges to the true state under certain conditions. Chakraborty et al [26] extended the results of Battilotti et al [25] to cooperative relative localisation using range measurements. They proposed an algorithm that utilises a distributed consensus approach and that is based on weighted least-squares optimisation.…”

“…Chakraborty et al. [26] extended the results of Battilotti et al. [25] to cooperative relative localisation using range measurements.…”

Robust Localization with Bounded Noise: Creating a Superset of the Possible Target Positions via Linear-Fractional Representations

“…Recently, Soares and Gomes [15] have proposed a distributed Huber-based point estimator for range measurements corrupted with Gaussian measurement noise, and non-Gaussian, long-tail noise, modelled as Laplace or Cauchy noise. The work in [16] uses rangeonly data to initialize an extended Kalman Filter for sensor fusion data.…”

Cooperative localisation of UAV swarm based on adaptive SA-PSO algorithm

“…In a UAV swarm, each individual UAV can be regarded as an intermediary node of communication transmission, which can process information and achieve cooperation by a communication network. [9] Moreover, based on the UAV swarm communication interaction, cooperative localisation realizes positioning by comprehensively using the measurement information of each UAV, resulting in a high-precision improvement in the absence of reliable information [10,11,12]. As a result, cooperative localisation, which makes full use of relative information within a swarm, has been attracting rising interest.…”