Indoor Localization for Autonomous Robot Navigation
Sean Kouma, Rachel Masters
TL;DR
This work investigates indoor localization for autonomous robot navigation using RSSI-based Wi‑Fi fingerprinting and machine learning. The authors collect a targeted RSSI dataset on a university floor, develop a three-layer DNN to map RSSI features to 2D coordinates, and deploy the model on an Nvidia Jetson Nano-based robot, integrating an A* path planner to reach destinations. Despite achieving a measured $MAE$ of $0.14$ (about $1.68$ ft) and enabling checkpoint-based navigation, the system attains only ~50% success in navigating corners, highlighting the gap between indoor RSSI signals and robust autonomous control. The results validate the feasibility of RSSI-driven indoor autonomous navigation and outline practical challenges—data collection, embedded deployment, sensor latency, and steering accuracy—that require further research for reliable real-world use.
Abstract
Indoor positioning systems (IPSs) have gained attention as outdoor navigation becomes prevalent in everyday life. Research is being actively conducted on how indoor smartphone navigation can be accomplished and improved using received signal strength indication (RSSI) and machine learning (ML). IPSs have more use cases that need further exploration, and we aim to explore using IPSs for the indoor navigation of an autonomous robot. We collected a dataset and trained models to test on a robot. We also developed an A* path-planning algorithm so that our robot could navigate itself using predicted directions. After testing different network structures, our robot was able to successfully navigate corners around 50 percent of the time. The findings of this paper indicate that using IPSs for autonomous robots is a promising area of future research.