A Visual Positioning System for Indoor Blind Navigation

Abstract

This paper presents a visual positioning system (VPS) for real-time pose estimation of a robotic navigation aid (RNA) for assistive navigation. The core of the VPS is a new method called depth-enhanced visual-inertial odometry (DVIO) that uses an RGB-D camera and an inertial measurement unit (IMU) to estimate the RNA’s pose. The DVIO method extracts the geometric feature (the floor plane) from the camera’s depth data and integrates its measurement residuals with that of the visual features and the inertial data in a graph optimization framework for pose estimation. A new measure based on the Sampson error is introduced to describe the measurement residuals of the near-range visual features with a known depth and that of the far-range visual features whose depths are unknown. The measure allows for the incorporation of both types of visual features into graph optimization. The use of the geometric feature and the Sampson error improves pose estimation accuracy and precision. The DVIO method is paired with a particle filter localization (PFL) method to locate the RNA in a 2D floor plan and the information is used to guide a visually impaired person. The PFL reduces the RNA’s position and heading error by aligning the camera’s depth data with the floor plan map. Together, the DVIO and the PFL allow for accurate pose estimation for wayfinding and 3D mapping for obstacle avoidance. Experimental results demonstrate the usefulness of the RNA in assistive navigation in indoor spaces.