Indoor Localization with Multi-Rate Extended Kalman Filter

Abstract

Localization is one of the most fundamental competencies required by an autonomous robot, providing crucial information about its position for decision-making in indoor environments. In the current literature, an indoor localization system utilizes exteroceptive sensors such as GNSS(Global Navigation Satellite System), a camera, or an ultrasonic sensor and onboard sensors such as odometry or accelerometer to observe its environment. These sensor data are typically fused using Extended Kalman Filter (EKF) techniques. To solve the challenges posed by multiple sensors, different EKF variants such as Multi-Rate EKF and Single-Rate EKF have been proposed. Additionally, localization architectures like Cascade and OWA (Ordered Weighted Averaging) have been introduced to enhance the fusion process.

This thesis aims to develop an indoor localization system for Elisa-3 robots. The proposed localization system is evaluated through simulations and real-world experiments conducted using the Elisa-3 robots platform. Furthermore, the effectiveness of the localization system is assessed in a control task involving the formation of a consensus within a robot swarm.

The simulated and experimental results indicate that using a Single-Rate Extended Kalman Filter under an OWA architecture can generate an accurate and precise trajectory across a wide range of scenarios.