Feature matching is at the base of many computer vision algorithms such as SLAM, which is a technology widely used in the area from intelligent vehicles (IV) to assistance for the visually impaired (VI). This article presents an improved detector and a novel semantic-visual descriptor, coined SORB (Semantic ORB), combining binary semantic labels and traditional ORB descriptor. Compared to the original ORB feature, the new SORB performs better in uniformity of distribution and accuracy of matching. We demonstrate it through experiments on some open source datasets and several real-world images obtained by RealSense.
In recent years, with development of computer vision and robotics, a wide variety of localization approaches have been proposed. However, it is still challenging to design a localization algorithm that performs well in both indoor and outdoor environment. In this paper, an algorithm that fuses camera, IMU, GPS, as well as digital compass is proposed to solve this problem. Our algorithm includes two phases: (1) the monocular RGB camera and IMU are fused together as a VIO that estimates the approximate orientation and position; (2) the absolute position and orientation measured by GPS and digital compass are merged with the position and orientation estimated in first phase to get a refined result in the world coordinate. A bag-of-word based algorithm is utilized to realize loop detection and relocalization. We also built a prototype and did two experiments to evaluate the effectiveness and robustness of the localization algorithm in both indoors and outdoors environment.
Detecting and reminding of crosswalks at urban intersections is one of the most important demands for people with visual impairments. A real-time crosswalk detection algorithm, adaptive extraction and consistency analysis (AECA), is proposed. Compared with existing algorithms, which detect crosswalks in ideal scenarios, the AECA algorithm performs better in challenging scenarios, such as crosswalks at far distances, low-contrast crosswalks, pedestrian occlusion, various illuminances, and the limited resources of portable PCs. Bright stripes of crosswalks are extracted by adaptive thresholding, and are gathered to form crosswalks by consistency analysis. On the testing dataset, the proposed algorithm achieves a precision of 84.6% and a recall of 60.1%, which are higher than the bipolarity-based algorithm. The position and orientation of crosswalks are conveyed to users by voice prompts so as to align themselves with crosswalks and walk along crosswalks. The field tests carried out in various practical scenarios prove the effectiveness and reliability of the proposed navigation approach.