Real-Time Stair Angle Estimation Based on 3D Dynamic Analysis for an Omnidirectional Autonomous Electric Wheelchair Realizing Illuminance-Independent Multiple Obstacle Detection and Stair Climbing

This study proposes a novel stair recognition method that integrates a monocular camera and a laser to improve the safety of the stair-climbing function in an omnidirectional autonomous electric wheelchair equipped with three Mecanum wheels mounted on a single axle. We performed three-dimensional stair measurements, estimated the angle of descent using a camera and laser, and built an automatic stair angle adjustment function into the wheelchair. The proposed method uses coordinate points to detect the staircase structure in three dimensions (x,y,z) , performs distance transformation to achieve high-accuracy three-dimensional distance estimation, and provides a detailed visualization of the staircase geometry. Estimating the descent angle from the obtained 3D data yielded a maximum error of 2.72° and an average error of 1.04°, demonstrating higher accuracy than a stereo camera. Furthermore, the automatic stair angle adjustment function of the proposed wheelchair was validated, and an algorithm was developed to automatically maintain the wheelchair’s horizontal orientation based on the acquired stair angle. The experimental results confirmed that the proposed method can accurately adjust in real time with varying staircase angles, significantly improving the safety of the stair-climbing function. In addition, by applying this method to an autonomous mobile robot, it can detect obstacles and recognize staircase structures in the absence of ambient illumination, allowing for autonomous operation while analyzing its environment in three dimensions.