Indoor Positioning and Localisation System with Sensor Fusion : AN IMPLEMENTATION ON AN INDOOR AUTONOMOUS ROBOT AT ÅF

Sammanfattning: This thesis will present guidelines of how to select sensors and algorithms for indoor positioning and localisation systems with sensor fusion. These guidelines are based on an extensive theory and state of the art research. Different scenarios are presented to give some examples of proposed sensors and algorithms for certain applications. There are of course no right or wrong sensor combinations, but some factors are good to bear in mind when a system is designed. To give an example of the proposed guidelines a Simultaneous Localisation and Mapping (SLAM) system as well as an Indoor Positioning System (IPS) has been designed and implemented on an embedded robot platform. The implemented SLAM system was based on a FastSLAM2 algorithm with ultrasonic range sensors and the implemented IPS was based on a WiFi RSS profiling method using aWeibull-distribution. The methods, sensors and infrastructure have been chosen based on requirements derived from wishes from the stakeholder as well as knowledge from the theory and state of the art research. A combination of SLAM and IPS is proposed, chosen to be called WiFiSLAM, in order to reduce errors from both of the methods. Unfortunately, due to unexpected issues with the platform, no combination has been implemented and tested. The systems were simulated independently before implemented on the embedded platform. Results from these simulations indicated that the requirements were able to be fulfilled as well as an indication of the minimum set-up needed for the implementation. Both the implemented systems were proven to have the expected accuracies during testing and with more time, better tuning could have been performed and probably also better results. From the results, a conclusion could be drawn that a combined WiFi SLAM solution would have improved the result in a larger testing area than what was used. IPS would have increased its precision and SLAM would have got an increased robustness. The thesis has shown that there is no exact way of finding a perfect sensor and method solution. Most important is, however, the weight between time, cost and quality. Other important factors are to decide in which environment a system will perform its tasks and if it is a safety critical system. It has also been shown that fused sensor data will outperform the result of just one sensor and that there is no max limit in fused sensors. However, that requires the sensor fusion algorithm to be well tuned, otherwise the opposite might happen.