Relative Localization of an Intelligent Robotic Walker based on Signal Fusion by Extended Information Filter
Smart Walker, Odometry, Calibration, Extended Information Filter, Localization, Inertial Measurement Unit, Encoders.
This work presents the development and improvement of a prototype smart walker designed to assist people with reduced mobility during physical therapy rehabilitation. The device is based on an adaptation of a conventional walker, integrating geared motors, an Arduino Mega microcontroller, a BeagleBone Blue microcomputer, and sensors such as incremental encoders on the wheels, an inertial measurement unit (IMU) composed of an accelerometer, gyroscope, and magnetometer, and a Kinect RGB-D camera. The main focus of the research is to obtain reliable estimates of the walker’s position and orientation through odometry, its calibration, and sensor fusion. A gyroscope and a magnetometer are employed in this study, although the approach is applicable to other complementary sensors. Using motion and observation models, the Extended Information Filter (EIF) was implemented, allowing for efficient integration of sensor data and reducing the effects of noise and uncertainty, representing a promising framework for future comprehensive localization. The adopted approach demonstrated that, even without external sources of absolute location, it is possible to accurately estimate the walker’s trajectory and orientation, provided the sensors are calibrated and properly combined via EIF. This strategy, which combines odometry with sensor fusion, represents an important step in the development of a robust, reliable localization system applicable to real-world assistive scenarios.