Doorway detection for autonomous indoor navigation of unmanned vehicles

Abstract

Fully autonomous navigation of unmanned vehicles, without relying on pre-installed tags or markers, still remains a challenge for GPS-denied areas and complex indoor environments. Doors are important for navigation as the entry/exit points. A novel approach is proposed to autonomously detect™ doorways by using the Project Tango platform. We first detect the candidate door openings from the 3D point cloud, and then use a pre-trained detector on corresponding RGB image regions to verify if these openings are indeed doors. We employ Aggregate Channel Features for detection, which are computationally efficient for real-time applications. Since detection is only performed on candidate regions, the system is more robust against false positives. The approach can be generalized to recognize windows, some architectural structures and obstacles. Experiments show that the proposed method can detect open doors in a robust and efficient manner.