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 algorithm 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. Experimentally, a range accuracy of < 1.7 mm (1σ) was achieved on a 1 × 2 m sample using miniaturised EMATs operating at a wavelength of 22 mm.Occupancy grid map is a popular tool for representing the surrounding environments of mobile robots/intelligent vehicles. It is shown that the proposed mapping algorithm successfully estimates the position of a sample's edges. The principle is demonstrated in both simulation and laboratory-based experiments. A Bayesian mapping technique (Occupancy grid mapping) was used to map the boundaries of an irregular sample in a pseudo-pulse-echo mode. Shear Horizontal (SH) guided waves generated by Electro-Magnetic Acoustic Transducers (EMATs) are used for mapping steel samples with a nominal thickness of 10 mm. It considers the specific problem of mapping geometric features using the guided ultrasonic waves, which enables the localisation of edges and/or the welded joints. Experimentally, a range accuracy of < 1.7 mm (1σ) was achieved on a 1 × 2 m sample using miniaturised EMATs operating at a wavelength of 22 mm.ĪB - This paper evaluates the benefits of using ultrasonic guided waves for the mapping of a structure, when implemented on a mobile magnetic robotic platform. N2 - This paper evaluates the benefits of using ultrasonic guided waves for the mapping of a structure, when implemented on a mobile magnetic robotic platform. T1 - Application of ultrasonic guided waves to robotic occupancy grid mapping