Pseudo-linear measurement approach for heterogeneous multi-robot relative localization

Abstract

The purpose of relative localization (RL) is to locate and track one or more robots in another moving robot body-fixed coordinate frame using relative range and/or bearing measurements. Most available RL methods assume known initial conditions at the first encounter of an arbitrary robot, and the tracking is then followed using an extended Kalman filter (EKF). In case of poor filter initialization, these EKF based methods sometimes cause instability or demand longer settling time. To overcome this issue, this paper proposes a pseudolinear measurement (PM) based technique for RL where true nonlinear measurements are algebraically transformed into PM.The proposed RL scheme is tested in Monte Carlo simulations for a heterogeneous multi-robot system comprising both aerial and ground robots. Results demonstrate that the proposed method performs RL with 5∼10 cm positional accuracy and 0.075∼0.1 rad orientational accuracy. The performance of the PM based RL is then compared against traditional EKF based methods with unknown filter initialization. The results demonstrate that the proposed method able to achieve both the positional and orientational accuracy within 12 iterations, whereas the traditional methods requires more than 250 iterations to achieve the same accuracy. The experiment validation of the proposed method was performed and results are congruent with the simulations.

Distributed Leader-Assistive Localization Method for a Heterogeneous Multirobotic System

“…Child robot's pose can be estimated either on a given coordinate frame (or global coordinate frame) [8] or on the body-fixed coordinate system of leader robots [1], [29], [30]. The former can either be performed in a distributed manner or centralized manner.…”

“…Different inter-robot-relative-measurement approaches have been investigated for multirobot cooperative localization: relative range-only [35], [36], relative bearing-only [37], [38], both relative range and relative bearing [6], [29], and full relative pose measurement [4], [5], [17], [22], [23], [27]. Work presented in [39] evaluated the effect of various relative measurement approaches on the accuracy of the cooperative localization.…”

Distributed collaborative localization for a heterogeneous multi-robot system

“…To overcome the bias problem associated with explicit linearization and the longer settling time problem associated with unknown filter initialization, the nonlinear relative range and bearing measurement model is algebraically converted to pseudo-linear format prior to the measurement update step of the relative localization [7]. The pseudolinear measurement model and the corresponding zero-mean temporally uncorrelated measurement noise covariance matrix are given in (3) and (4) respectively.…”

Nonlinear moving horizon state estimation for multi-robot relative localization

“…To overpass these issues, the majority of past work assumed a known initial condition for RL between two arbitrary robots [4]. In addition to EKF, particle filter (PF) [5] and unscented Kalman filter (UKF) [6], and pseudolinear Kalman filter [7], [8] have been also used to achieve RL in collaboration tasks.…”