Stair detection is another important depth characteristic analysis technol-ogy. This algorithm can help the blind to go upstairs or downstairs if any stair locates in the vicinity to the blind. Similar to chapter 3.3, the tech-nology of depth analysis is reliable and efficient. Furthermore, the proposed algorithm can also inform the blind how far is the distance from the stair.
It is an useful information to the blind. In this section, we will introduce the stair detection algorithm by depth analysis.
There are some researches for stair detection in robotic system or com-puter vision field [37] [38] [39]. Similar to chpater 3.3, these researchers use edge, gradient or other characteristics in 2D image to extract the stair.
These algorithms could help us to grab the stair region but not suitable in our system. The same reason is that the complexity is too high, our application only have limited computational resource to detect the stair.
The second reason is that our system equip 3D sensor, it can largely reduce the unnecessary calculation like coordination transformation. Therefore, we proposed an high efficient stair detection algorithm in our system, that will be introduced in the following paragraph.
Stair usually contain a lot of small vertical and horizontal planes and these planes would cross each others. For example, the horizontal plane would locate beneath and under some vertical plane, this characteristic would regularly repeat in stair until the end. According to this characteris-tic, we can individually employ the detection algorithm in chapter 3.2 and chapter 3.3, then combining these two algorithms to determine if some re-gion detected by 3.2 and 3.3 is stair or not. Fig. 3.8 shows the interlaced image of vertical and horizontal planes, and Fig. 3.9 shows the final results of stair detection. The proposed algorithm achieves 96.05% detection rate and 0.8% false alarm rate.
Figure 3.9: The right side is the final results of stair detection and the left side is the corresponding interlaced planes in depth image.
3.5 Conclusion
We propose three depth characteristic analysis algorithm for road, wall and stair detection. These functions are useful to the blind when they are in the unknown environment. The proposed method use depth cue instead
33 of RGB cue, the traditional approach, to analyze the characteristics. The advantage is that the shape of object is usually invariable. For example, the appearance of wall usually be painted with different color, however, the shape of wall only be vertical and flat. That is why depth analysis is reliable in our system. The experimental results could also prove the robustness.
In road detection algorithm, Sec. 3.2, the detection rate is above 93% and the false alarm rate is 3.25%. In wall detection algorithm, Sec. 3.3, the detection rate is about 94.05% and the false alarm rate is 2.3%. In stair detection, Sec. 3.4, our proposed algorithm achieves 96.05% detection rate and 0.8% false alarm rate. In conclusion, we establish a practical framework for visually-impaired aid applications in depth characteristic analysis.
Chapter 4
Position Reconstruction by Street-View Recognition
4.1 Introduction
Self-localization is important in many applications, such as automatic vehi-cle or pedestrian navigation, robotic path planning, and visually-impaired electronic-aids. The GPS system, which provides localization and huge map scale, is widely utilized in these applications. However, GPS has a fatal defect, positioning inaccuracy, which may be caused by satellite masking, multipath or cloudy weather. The positioning error may increase up to 20 meters when the user moves in urban environments. For many applications, such as navigation services for pedestrian, robot or the visually-impaired, 20 meters is unacceptable. An example is shown in Fig. 4.1. If a blind person is guided by a GPS system to get to his destination, the inaccurate position would lead him toward the opposite direction. To overcome this problem, an accurate positioning system is important and must be a basic requirement of GPS-based systems for advanced applications.
Many works have been proposed to improve GPS accuracy. These works can be classified into three categories. (1) Several works utilized
mathemati-35
Wrong location by GPS Correct location Wrong navigation Correct navigation
Destination
Figure 4.1: The scenario of incorrect positioning estimation
cal models, such as Kalman filter [40], least square model [41] and frequency domain model [42], to eliminate the noise of positioning estimation and pre-dict the most possible location. In general, these models do not have good performance in urban environments because the satellite signal is signifi-cantly masked or reflected by crowded buildings. (2) There are also some works combining different sensors to acquire multi-type data. For exam-ple, Maya Dawood [43] proposed a vehicle localization system with fusion of an odometer, reckoning sensors, 3D models and visual recognition meth-ods. The approaches of the category are accurate but multi-type data fusion
37 from too many sensors makes the system more complex. (3) Some works [44]
[45] adopted visual recognition methods by tracking corresponding points across neighboring video frames for camera pose estimation. Shortages of these works are the assumption of the known initial location and error es-timations propagated during a longer period.
In our work, we propose an accurate and robust positioning system based on street view recognition. Our system is equipped with a GPS receiver and a digital compass to catch global map information and estimate the current user orientation, respectively. Vision-based recognition technique is employed to capture dynamic street views, such as shop or building signs, which are tagged within the GPS map. With the targeted signs around the user on the street, a view-angle invariant distance estimation mechanism is developed to infer accurate user locations. The proposed system can be applied to many advanced applications, such as robot self-localization, visually-impaired aids, augmented reality and general navigation on smart phones.