Indoor Localization with Multi-Rate Extended Kalman Filter

Indoor Localization with Multi-Rate Extended Kalman Filter: Based on Elisa-3 robot

Li, Yiting (TU Delft Electrical Engineering, Mathematics and Computer Science)

Mazo, M. (mentor)

Delft University of Technology

Electrical Engineering | Embedded Systems

2023-06-05

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.

Extended Kalman Filter
Multi-Rate
Sensor fusion
Position estimation

http://resolver.tudelft.nl/uuid:5ffff5d8-d1c8-4513-92c4-881f4b344558

Student theses

master thesis

© 2023 Yiting Li