Indoor localization of a mobile robot using sensor fusion : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Mechatronics with Honours at Massey University, Wellington, New Zealand

Reliable indoor navigation of mobile robots has been a popular research topic in recent years. GPS systems used for outdoor mobile robot navigation can not be used indoor (warehouse, hospital or other buildings) because it requires an unobstructed view of the sky. Therefore a specially designed indoor localization system for mobile robot is needed. This project aims to develop a reliable position and heading angle estimator for real time indoor localization of mobile robots. Two different techniques have been developed and each consisted of three different sensor modules based on infrared sensing, calibrated odometry and calibrated gyroscope. Integration of these three sensor modules is achieved by applying the real time Kalman filter which provides filtered and reliable information of a mobile robot's current location and orientation relative to its environment. Extensive experimental results are provided to demonstrate its improvement over conventional methods like dead reckoning. In addition, a control strategy is developed to control the mobile robot to move along the planned trajectory. The techniques developed in this project have potentials for the application for mobile robots in medical service, health care, surveillances, search and rescue in indoor environments

Open Source Robot Localization for Non-Planar Environments

The operational environments in which a mobile robot executes its missions often exhibit non-flat terrain characteristics, encompassing outdoor and indoor settings featuring ramps and slopes. In such scenarios, the conventional methodologies employed for localization encounter novel challenges and limitations. This study delineates a localization framework incorporating ground elevation and inclination considerations, deviating from traditional 2D localization paradigms that may falter in such contexts. In our proposed approach, the map encompasses elevation and spatial occupancy information, employing Gridmaps and Octomaps. At the same time, the perception model is designed to accommodate the robot's inclined orientation and the potential presence of ground as an obstacle, besides usual structural and dynamic obstacles. We have developed and rigorously validated our approach within Nav2, and esteemed open-source framework renowned for robot navigation. Our findings demonstrate that our methodology represents a viable and effective alternative for mobile robots operating in challenging outdoor environments or intrincate terrains

A Real-Time Robust Global Localization for Autonomous Mobile Robots in Large Environments

International audienceGlobal localization aims to estimate a robot's pose in a learned map without any prior knowledge of its initial pose. Achieving highly accurate global localization remains a challenge for autonomous mobile robots especially in large-scale unstructured outdoor environments. This paper introduces a real-time reliable global localization approach with the capability of addressing the kidnapped robot problem using only laser sensors. Our approach includes four steps: 1) local Simultaneous Localization and Mapping 2) map matching 3) position tracking and 4) localization quality evaluation. For sensor perception, we use occupancy grid method to represent robot environment. A novel pyramid grid-map based coarse-to-fine matching approach is proposed to improve the localization accuracy. Experimental results including an outdoor environment of 25,000 m2 are presented to validate the feasibility and reliability of the proposed approach

Distributed and adaptive location identification system for mobile devices

Indoor location identification and navigation need to be as simple, seamless, and ubiquitous as its outdoor GPS-based counterpart is. It would be of great convenience to the mobile user to be able to continue navigating seamlessly as he or she moves from a GPS-clear outdoor environment into an indoor environment or a GPS-obstructed outdoor environment such as a tunnel or forest. Existing infrastructure-based indoor localization systems lack such capability, on top of potentially facing several critical technical challenges such as increased cost of installation, centralization, lack of reliability, poor localization accuracy, poor adaptation to the dynamics of the surrounding environment, latency, system-level and computational complexities, repetitive labor-intensive parameter tuning, and user privacy. To this end, this paper presents a novel mechanism with the potential to overcome most (if not all) of the abovementioned challenges. The proposed mechanism is simple, distributed, adaptive, collaborative, and cost-effective. Based on the proposed algorithm, a mobile blind device can potentially utilize, as GPS-like reference nodes, either in-range location-aware compatible mobile devices or preinstalled low-cost infrastructure-less location-aware beacon nodes. The proposed approach is model-based and calibration-free that uses the received signal strength to periodically and collaboratively measure and update the radio frequency characteristics of the operating environment to estimate the distances to the reference nodes. Trilateration is then used by the blind device to identify its own location, similar to that used in the GPS-based system. Simulation and empirical testing ascertained that the proposed approach can potentially be the core of future indoor and GPS-obstructed environments

Vision for mobile robot localization in urban environments

This paper addresses the problem of mobile robot localization in urban environments. Typically, GPS is the preferred sensor for outdoor operation. However, using GPS-only localization methods leads to significant performance degradation in urban areas where tall nearby structures obstruct the clear view of the satellites. In our work, we use vision-based techniques to supplement GPS and odometry and provide accurate localization. The vision system identifies prominent linear features in the scene and matches them with a reduced model of nearby buildings, yielding improved pose estimation of the robot