Mobile Robot Localisation for Indoor Environments Based on Ceiling Pattern Recognition

Abstract

In this paper a multi-modal localisation system, that estimates a robot position in indoor environments using only on-board sensors, namely a webcam and a compass, is presented. Ceiling lights are used as beacons. Their position is previously known or self-learned during normal operation. Markov Localisation (ML) is both simulated and experimentally validated. For the prediction step it combines IMU (Inertial Measurement Unit) data and image parameters to compute the attitude of the robot. The update step is then calculated by measuring the distance to possibly visible ceiling lights. The experimental validation of the proposed solution shows that the robot position estimate converges to its real position and the error is kept within decimetres of magnitude.

A Performance Comparison between Different Industrial Real-Time Indoor Localization Systems for Mobile Platforms

“…Another approach is to use computer vision techniques [38] to localize the robot or other platform. This can involve using visual features such as roofs [39], corners [40], edges, textures, or fiducial markers [41] in the environment to determine the robot's position and orientation. This approach is generally more flexible and can work in a variety of environments, but it may be less accurate than other methods, particularly in cluttered or poorly lit environments.…”

Navigation System for Mobile Robots Using PCA-Based Localization from Ceiling Depth Images: Experimental Validation

“…Usually robots achieve their location extracting specific features from the environment or detecting artificial landmarks. Although the most common approach is to extract features looking around [1], some robots use a camera looking upward [2]- [4]. The use of ceiling vision has the advantage that images can be considered without scaling; are less affected by occlusions due to obstacles moving around; and are usually static, increasing the mapping database reliability.…”