Vision-based Multi-MAV Localization with Anonymous Relative Measurements Using Coupled Probabilistic Data Association Filter

TL;DR

This paper presents a novel vision-based multi-robot localization method that combines onboard visual-inertial odometry with anonymous robot detection using an extended probabilistic data association filter, effective even with noisy and uncertain measurements.

Contribution

It extends the Coupled Probabilistic Data Association Filter to handle nonlinear measurements for multi-MAV localization without external infrastructure.

Findings

Outperforms simple VIO-based methods in simulations

Handles false positives and negatives in robot detection

Enables formation flight using onboard sensing and estimation

Abstract

We address the localization of robots in a multi-MAV system where external infrastructure like GPS or motion capture systems may not be available. Our approach lends itself to implementation on platforms with several constraints on size, weight, and power (SWaP). Particularly, our framework fuses the onboard VIO with the anonymous, visual-based robot-to-robot detection to estimate all robot poses in one common frame, addressing three main challenges: 1) the initial configuration of the robot team is unknown, 2) the data association between each vision-based detection and robot targets is unknown, and 3) the vision-based detection yields false negatives, false positives, inaccurate, and provides noisy bearing, distance measurements of other robots. Our approach extends the Coupled Probabilistic Data Association Filter (CPDAF)[1] to cope with nonlinear measurements. We demonstrate the superior performance of our approach over a simple VIO-based method in a simulation with the measurement models statistically modeled using the real experimental data. We also show how onboard sensing, estimation, and control can be used for formation flight.