Indoor Localization for an Autonomous Model Car: A Marker-Based Multi-Sensor Fusion Framework

TL;DR

This paper develops a multi-sensor fusion framework for indoor localization of an autonomous model car, combining fiducial markers, inertial sensors, and wheel odometry, validated with a low-cost ground truth method.

Contribution

It introduces a novel integrated localization framework with adaptive filtering and outlier detection for indoor autonomous vehicles, validated through experimental testing.

Findings

Robust indoor localization achieved with sensor fusion.

Adaptive noise tuning improves filter performance.

Effective ground truth validation with a single LiDAR.

Abstract

Global navigation satellite systems readily provide accurate position information when localizing a robot outdoors. However, an analogous standard solution does not exist yet for mobile robots operating indoors. This paper presents an integrated framework for indoor localization and experimental validation of an autonomous driving system based on an advanced driver-assistance system (ADAS) model car. The global pose of the model car is obtained by fusing information from fiducial markers, inertial sensors and wheel odometry. In order to achieve robust localization, we investigate and compare two extensions to the Extended Kalman Filter; first with adaptive noise tuning and second with Chi-squared test for measurement outlier detection. An efficient and low-cost ground truth measurement method using a single LiDAR sensor is also proposed to validate the results. The performance of the localization algorithms is tested on a complete autonomous driving system with trajectory planning and model predictive control.