Vision-Based Autonomous Vehicle Guidance for Indoor Security Patrolling by a SIFT-Based Vehicle-Localization Technique

Abstract

A novel method for guidance of vision-based autonomous vehicles for indoor security patrolling using scale-invariant feature transformation (SIFT) and vehicle localization techniques is proposed. Along-path objects to be monitored are used as landmarks for vehicle localization. The localization work is accomplished by three steps: SIFT-based object image feature matching, 2-D affine transformation using the Hough transform, and analytic 3-D space transformation. Object monitoring can be simultaneously achieved during the vehicle-localization process, and most planar-surfaced objects can be utilized in the process, greatly enhancing the applicability of the proposed method. Vehicle trajectory deviations from the planned path due to mechanic error accumulation are also estimated by setting up a calibration line on the monitored object image and applying the 3-D space transformation. Moreover, a path-correction technique is proposed to conduct a path adjustment and guide the vehicle to navigate to the next path node. Analysis of the accuracy of the vehicle-localization and path-correction results is finally included. The experimental results show that the proposed method, utilizing only a single view of each object, can guide the vehicle to navigate accurately and monitor objects successfully.