Infradar-Localization: Single-Chip Infrared- and Radar-Based Monte Carlo Localization

This paper proposes a novel approach for indoor robot localization that leverages a fusion of information from single-chip infrared (Time-of-Flight) and radar sensors. The aim of our research is the development of a cost-effective and lightweight system that can achieve high-precision robot localization. Unlike traditional localization methods based on LiDARs or cameras, our proposed system uses single-chip infrared and radar sensors to overcome the limitations of high cost and bulky hardware. Specifically, we employ a Doppler radar-based velocity motion model for the estimation of the robot's ego-motion, eliminating the need for additional sensors such as IMU or wheel encoders. Next, we describe a hybrid sensor model for single-chip infrared and radar sensors that provides robust and accurate environmental perception with dynamic outlier removal. Finally, we integrate these components into a Monte Carlo localization framework to generate accurate real-time estimation of the robot's position and orientation. This is the first time a single-chip infrared and radar fusion-based framework has been applied to robot localization, to the best of our knowledge. Through a comprehensive experimental evaluation, we demonstrate the system's high accuracy and efficiency, achieving an average localization error of 9 cm in diverse indoor environments. This remarkable performance, combined with the low-cost and lightweight nature of our proposed solution, positions it as a highly promising alternative for a wide range of applications, including robotics, smart homes, and autonomous vehicles. The significant advancements of this novel approach offer vast potential to revolutionize the field of localization, enabling more precise and cost-effective navigation systems.