Sensor Fusion of Camera, GPS and IMU using Fuzzy Adaptive Multiple Motion Models

TL;DR

This paper presents a sensor fusion system combining camera, GPS, and IMU data using fuzzy adaptive models to enhance outdoor tracking accuracy for AR applications, outperforming traditional methods.

Contribution

It introduces a novel fuzzy rule-based approach for adaptive model selection in sensor fusion, improving accuracy and convergence speed in outdoor tracking.

Findings

Fuzzy adaptive models improve tracking accuracy over conventional methods.

The system achieves faster convergence in pose estimation.

Application demonstrated in cultural heritage context.

Abstract

A tracking system that will be used for Augmented Reality (AR) applications has two main requirements: accuracy and frame rate. The first requirement is related to the performance of the pose estimation algorithm and how accurately the tracking system can find the position and orientation of the user in the environment. Accuracy problems of current tracking devices, considering that they are low-cost devices, cause static errors during this motion estimation process. The second requirement is related to dynamic errors (the end-to-end system delay; occurring because of the delay in estimating the motion of the user and displaying images based on this estimate. This paper investigates combining the vision-based estimates with measurements from other sensors, GPS and IMU, in order to improve the tracking accuracy in outdoor environments. The idea of using Fuzzy Adaptive Multiple Models (FAMM) was investigated using a novel fuzzy rule-based approach to decide on the model that results in improved accuracy and faster convergence for the fusion filter. Results show that the developed tracking system is more accurate than a conventional GPS-IMU fusion approach due to additional estimates from a camera and fuzzy motion models. The paper also presents an application in cultural heritage context.