# A Novel Loosely Coupled Collaborative Localization Method Utilizing Integrated IMU-Aided Cameras for Multiple Autonomous Robots

## TL;DR

This paper introduces a new method for multiple autonomous robots to accurately estimate their positions using IMU-aided cameras in a collaborative way.

## Contribution

The novel CICEKF method enables robust and adaptable collaborative localization for multiple robots using a loosely coupled two-step structure.

## Key findings

- The first step improves robustness by fusing IMU and visual data on the velocity level.
- The second step reliably estimates relative robot positions and orientations.
- Simulations and experiments confirm the method's effectiveness for standalone and collaborative use.

## Abstract

IMUs (inertial measurement units) and cameras are popular sensors for autonomous localization due to their convenient integration. This article proposes a collaborative localization method, the CICEKF (collaborative IMU-aided camera extended Kalman filter), with a loosely coupled and two-step structure for the autonomous locomotion estimation of collaborative robots. The first step is for single-robot localization estimation, fusing and connecting the IMU and visual measurement data on the velocity level, which can improve the robustness and adaptability of different visual measurement approaches without redesigning the visual optimization process. The second step is for estimating the relative configuration of multiple robots, which further fuses the individual motion information to estimate the relative translation and rotation reliably. The simulation and experiment demonstrate that both steps of the filter are capable of accomplishing locomotion estimation missions, standalone or collaboratively.