Combining Obstacle Avoidance and Visual Simultaneous Localization and Mapping for Indoor Navigation

Abstract

People with disabilities (PWD) face a number of challenges such as obstacle avoidance or taking a minimum path to reach a destination while travelling or taking public transport, especially in airports or bus stations. In some cases, PWD, and specifically visually impaired people, have to wait longer to overcome these situations. In order to solve these problems, the computer-vision community has applied a number of techniques that are nonetheless insufficient to handle these situations. In this paper, we propose a visual simultaneous localization and mapping for moving-person tracking (VSLAMMPT) method that can assist PWD in smooth movement by knowing a position in an unknown environment. We applied expected error reduction with active-semisupervised-learning (EER−ASSL)-based person detection to eliminate noisy samples in dynamic environments. After that, we applied VSLAMMPT for effective smoothing, obstacle avoidance, and uniform navigation in an indoor environment. We analyze the joint approach symmetrically and applied the proposed method to benchmark datasets and obtained impressive performance.

Keywords

• slam
• obstacle avoidance
• depth estimation
• object detection