Lightweight Gaussian Process-Based Visual Servoing for Autonomous Wheelchair Sidewalk Navigation

The personal mobility of wheelchair users is pivotal to their overall well-being. Navigating electric wheelchairs through confined spaces poses notable challenges, particularly for individuals with disabilities. In response, this research introduces a pioneering vision-based approach for sidewalk navigation that leverages tactile paving features and advanced machine learning models. While many existing solutions rely on expensive sensors or GPU-accelerated deep networks, we address this limitation by employing only a low-cost monocular camera and a lightweight GP model. Running at 7 Hz on a Raspberry Pi, our approach offers real-time autonomy without requiring specialized or high-end hardware. The proposed methodology includes creating a specialized dataset, formulating a custom control law, and deploying a lightweight real-time Gaussian Process (GP) model on a Raspberry Pi platform. Thorough experimentation substantiates the efficacy of the presented approach, demonstrating accurate and dependable autonomous wheelchair navigation on sidewalks. Beyond its technical contributions, the proposed solution offers a cost-effective alternative to conventional sensor-dependent systems, profoundly enhancing user mobility and quality of life. By equipping wheelchair users with enhanced navigation capabilities, this research strives to foster greater independence and inclusion in their daily lives.