In this letter, we research and evaluate end-to-end learning of monocular semantic-metric occupancy grid mapping from weak binocular ground truth. The network learns to predict four classes, as well as a camera to bird's eye view mapping. At the core, it utilizes a variational encoder–decoder network that encodes the front-view visual information of the driving scene and subsequently decodes it into a two-dimensional top-view Cartesian coordinate system. The evaluations on Cityscapes show that the end-to-end learning of semantic-metric occupancy grids outperforms the deterministic mapping approach with flat-plane assumption by more than 12 mean intersection-over-union. Furthermore, we show that the variational sampling with a relatively small embedding vector brings robustness against vehicle dynamic perturbations, and generalizability for unseen KITTI data. Our network achieves real-time inference rates of approx. 35 Hz for an input image with a resolution of 256 × 512 pixels and an output map with 64 × 64 occupancy grid cells using a Titan V GPU.
@cite_19: Occupancy grid filtering is important for navigation and localization in dynamic environments. The techniques that have been proposed in the past research, such as the Bayesian occupancy filter and the sequential Monte Carlo Bayesian occupancy filter, involve the independent consideration of all grid cells and, thus, are inaccurate and computationally expensive. To solve this problem, we propose a fast occupancy grid filtering method that uses a grid state map. We apply a superpixel-based clustering concept called a grid cell cluster (GCC) to 3D particles measured using light detection and ranging and dense depth maps extracted from a stereo vision sensor. The GCC locates a connection among similar data items according to certain criteria. In our proposed method, a GCC consisting of several grid cells has one dynamic state. Thus, we can reduce the computational cost and the number of computational errors by using the same prediction matrices is generated from the grid cells belonging to an object. Our method was evaluated on the KITTI benchmark data set. The results showed that the proposed method was faster (by approximately 38.9 ) than Mekahanachas method and approximately 12 more accurate (on the intersection-over-union metric) than prevalent techniques.
@cite_35: Automated guided vehicles require spatial representations of their working spaces in order to ensure safe navigation and carry out high-level tasks. Typically, these models are given by geometric m...
@cite_21: Occupancy grid map is a popular tool for representing the surrounding environments of mobile robots intelligent vehicles. Its applications can be dated back to the 1980s, when researchers utilized sonar or LiDAR to illustrate environments by occupancy grids. However, in the literature, research on vision-based occupancy grid mapping is scant. 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. 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. Besides representing the surroundings as occupancy grids, dynamic occupancy grid mapping could provide the motion information of the grids. The proposed framework consists of two components. The first is motion estimation for the moving vehicle itself and independent moving objects. The second is dynamic occupancy grid mapping, which is based on the estimated motion information and the dense disparity map. The main benefit of the proposed framework is the ability of mapping occupied areas and moving objects at the same time. This is very practical in real applications. The proposed method is evaluated using real data acquired by our intelligent vehicle platform “SeTCar” in urban environments.
@cite_7: In this paper we provide an overview of a new framework for robot perception, real-world modelling, and navigation that uses a stochastic tesselated representation of spatial information called the Occupancy Grid. The Occupancy Grid is a multi-dimensional random field model that maintains probabilistic estimates of the occupancy state of each cell in a spatial lattice. Bayesian estimation mechanisms employing stochastic sensor models allow incremental updating of the Occupancy Grid using multi-view, multi-sensor data, composition of multiple maps, decision-making, and incorporation of robot and sensor position uncertainty. We present the underlying stochastic formulation of the Occupancy Grid framework, and discuss its application to a variety of robotic tusks. These include range-based mapping, multi-sensor integration, path-planning and obstacle avoidance, handling of robot position uncertainty, incorporation of pre-compiled maps, recovery of geometric representations, and other related problems. The experimental results show that the Occupancy Grid approach generates dense world models, is robust under sensor uncertainty and errors, and allows explicit handling of uncertainty. It supports the development of robust and agile sensor interpretation methods, incremental discovery procedures, and composition of information from multiple sources. Furthermore, the results illustrate that robotic tasks can be addressed through operations performed di- rectly on the Occupancy Grid, and that these operations have strong parallels to operations performed in the image processing domain.
The occupancy grid map @cite_7 is one of the most popular local metric map representations for mobile robots. Besides range sensors such as RaDAR and LiDAR, occupancy grid maps can also be generated from RGB-D cameras @cite_35 , stereo vision @cite_21 , and from fusion of multiple sensors @cite_19 . However, the classical occupancy grid maps are without semantics, cells only have two possible states: occupied or not occupied.