It is very difficult for visually impaired people to perceive and avoid obstacles at a distance. To address this problem, the unified framework of multiple target detection, recognition, and fusion is proposed based on the sensor fusion system comprising a low-power millimeter wave (MMW) radar and an RGB-Depth (RGB-D) sensor. In this paper, the Mask R-CNN and the single shot multibox detector network are utilized to detect and recognize the objects from color images. The obstacles’ depth information is obtained from the depth images using the MeanShift algorithm. The position and velocity information on the multiple target is detected by the MMW radar based on the principle of a frequency modulated continuous wave. The data fusion based on the particle filter obtains more accurate state estimation and richer information by fusing the detection results from the color images, depth images, and radar data compared with using only one sensor. The experimental results show that the data fusion enriches the detection results. Meanwhile, the effective detection range is expanded compared to using only the RGB-D sensor. Moreover, the data fusion results keep high accuracy and stability under diverse range and illumination conditions. As a wearable system, the sensor fusion system has the characteristics of versatility, portability, and cost-effectiveness.

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.

It is very difficult for visually impaired people (VIP) to perceive and avoid obstacles at a distance. To address this problem, we propose a sensor fusion system, which combines the RGB-depth (RGB-D) sensor and millimeter wave (MMW) radar sensor, to perceive the surrounding obstacles. The position and velocity information of the multiple targets are detected by the MMW radar based on the principle of frequency modulated continuous wave. The depth and position information of the obstacles are verified by the RGB-D sensor based on the MeanShift algorithm. The data fusion based on the joint probabilistic data association algorithm and Kalman filter enable the navigation assistance system to obtain more accurate state estimates compared with using only one sensor. The nonsemantic stereophonic interface is utilized to transfer the obstacle detection results to the VIP. The experiment results show that multiple objects with different ranges and angles are detected by the radar and the RGB-D sensor. The effective detection range is expanded up to 80 m compared to using only the RGB-D sensor. Moreover, the measurement results are stable under diverse illumination conditions. As a wearable system, the sensor fusion system has the characteristics of versatility, portability, and cost-effectiveness.

The perception module plays an important role in vehicles equipped with advanced driver-assistance systems (ADAS). This paper presents a multi-sensor data fusion system based on the polarization color stereo camera and the forward-looking light detection and ranging (LiDAR), which achieves the multiple target detection, recognition, and data fusion. The You Only Look Once v4 (YOLOv4) network is utilized to achieve object detection and recognition on the color images. The depth images are obtained from the rectified left and right images based on the principle of the epipolar constraints, then the obstacles are detected from the depth images using the MeanShift algorithm. The pixel-level polarization images are extracted from the raw polarization-grey images, then the water hazards are detected successfully. The PointPillars network is employed to detect the objects from the point cloud. The calibration and synchronization between the sensors are accomplished. The experiment results show that the data fusion enriches the detection results, provides high-dimensional perceptual information and extends the effective detection range. Meanwhile, the detection results are stable under diverse range and illumination conditions.

Intersection navigation comprises one of the major ingredient of Intelligent Transportation Systems (ITS) for Visually Impaired Pedestrians (VIP), who are the most vulnerable road users that should be protected with a high priority in metropolitan areas. Robotic vision-based assistive technologies sprung up over the past few years, which focused on specific scene objects using monocular detectors or depth sensors. These dividual solutions have reached impressive detectable range and accuracy with relatively short running time, and enhanced the intersection perception to a large degree. However, simultaneously enabling all detectors incurs a long delay and becomes computationally prohibitive on wearable embedded systems. In this work, we propose to seize CNN-based per-pixel semantic segmenter to cover navigational perception needs in a unified way. This is not only critical to perceive crosswalk position (where to cross roads), traffic light signal (when to cross roads), but also to analyze the states of other pedestrians and vehicles (whether safe to cross roads). At the centroid of our unification proposal is a deep learning architecture, aspired to attain efficient and robust semantic understanding. A comprehensive variety of experiments demonstrates the advanced accuracy over state-of-art algorithms/segmenters while maintaining high inference speed on a real-world navigation assistance system.