![occupancy grid mapping with ultrasonic range finder occupancy grid mapping with ultrasonic range finder](https://www.researchgate.net/profile/Mingxi-Zhou/publication/311758035/figure/fig2/AS:613894083080192@1523375163039/Global-occupancy-map-generated-using-the-dynamic-inverse-sonar-model-Red-line-is-the.png)
The successfully tracked feature points are used to estimate the ego-motion of the vehicle itself and the independent motions of the surrounding moving objects. For every incoming stereo image pair, sparse image feature points are extracted and tracked in a circular manner between the current and previous image pairs. The proposed framework mainly comprises two components (motion analysis for the vehicle itself and independent moving objects and dynamic occupancy grid mapping) within two parallel processes (sparse feature points processing between two consecutive stereo image pairs and dense stereo processing). The dynamic occupancy grid map models real environments by evenly distributed rectangle grids, which contain both occupancy and motion information. This paper proposes a framework of stereo-vision-based dynamic occupancy grid mapping in urban environments. Therefore, the subsequent change emphasizes the ability of mapping in dynamic environments in real time without prior information. In our applications, an intelligent vehicle has to drive by itself in a dynamic, unknown urban area.
![occupancy grid mapping with ultrasonic range finder occupancy grid mapping with ultrasonic range finder](https://www.cs.cmu.edu/afs/cs/project/jair/pub/volume11/fox99a-html/img125.gif)
After the map is generated, it is stored for future usage, whereas the situation changes in applications of intelligent vehicles. In addition, in previous research, occupancy grid mapping is served by a static environment. However, in contrast to pervasive applications of visual systems in intelligent vehicles, occupancy grid mapping by visual systems is not well researched. Usually, under a given sensor measurement model (such as the inverse sensor model ), probabilistic occupancy grid mapping is able to be quickly calculated with the measurements. The characteristic of measuring distance directly makes occupancy grid mapping easily performed. In the literature, range sensors, such as LiDAR and radar, are usually used for creating occupancy grid maps. Besides, as a practical instrument for environmental understanding, the occupancy grid map is very useful for integrating different sensor measurements (radar, LiDAR, vision system) into a unified representation.
#Occupancy grid mapping with ultrasonic range finder free#
It maps the environment around a vehicle as a field of uniformly-distributed binary/ternary variables indicating the status of cells (occupied, free or undetected). The occupancy grid map (OGP) is one of the most popular environmental representation tools. In the field of intelligent vehicles, many tasks, such as localization, collision avoidance and path planning, are usually performed based on well-represented maps. The proposed method is evaluated using real data acquired by our intelligent vehicle platform “SeTCar” in urban environments. This is very practical in real applications. The main benefit of the proposed framework is the ability of mapping occupied areas and moving objects at the same time. The second is dynamic occupancy grid mapping, which is based on the estimated motion information and the dense disparity map. The first is motion estimation for the moving vehicle itself and independent moving objects. The proposed framework consists of two components. Besides representing the surroundings as occupancy grids, dynamic occupancy grid mapping could provide the motion information of the grids. The paper addresses this issue by presenting a stereo-vision-based framework to create a dynamic occupancy grid map, which is applied in an intelligent vehicle driving in an urban scenario. Furthermore, when moving in a real dynamic world, traditional occupancy grid mapping is required not only with the ability to detect occupied areas, but also with the capability to understand dynamic environments. However, in the literature, research on vision-based occupancy grid mapping is scant. Its applications can be dated back to the 1980s, when researchers utilized sonar or LiDAR to illustrate environments by occupancy grids. Occupancy grid map is a popular tool for representing the surrounding environments of mobile robots/intelligent vehicles.