Adaptive Measurement Model-Based Fusion of Capacitive Proximity Sensor and LiDAR for Improved Mobile Robot Perception

This study introduces a novel algorithm that combines a custom-developed capacitive proximity sensor with LiDAR. This integration targets the limitations of using single-sensor systems for mobile robot perception. Our approach deals with the non-Gaussian distribution that arises during the nonlinear transformation of capacitive sensor data into distance measurements. The non-Gaussian distribution resulting from this nonlinear transformation is linearized using a first-order Taylor approximation, creating a measurement model unique to our sensor. This method helps establish a linear relationship between capacitance values and their corresponding distance measurements. Assuming that the capacitance's standard deviation (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sigma$</tex-math></inline-formula>) remains constant, it is modeled as a distance function. By linearizing the capacitance data and synthesizing it with LiDAR data using Gaussian methods, we fuse the sensor information to enhance integration. This results in more precise and robust distance measurements than those obtained through traditional Extended Kalman Filter (EKF) and Adaptive Extended Kalman Filter (AEKF) methods. The proposed algorithm is designed for real-time data processing, significantly improving the robot's state estimation accuracy and stability in various environments. This study offers a reliable method for positional estimation of mobile robots, showcasing outstanding fusion performance in complex settings.