See TSetOfMetricMapInitializers::loadFromConfigFile for format specifications.
This node supports those simple maps, plus all other map kinds defined in MRPT. Most ROS packages expect the map to be a unique occupancy grid map.
The update probability of the binary Bayesian filter is currently hard-coded and requires a code change to be modified.(Optional) Future extensions / Unimplemented parts #
(Optional) Error detection and handling # (Optional) Performance characterization # (Optional) References/External links # In several places we have modified the external code written in BSD3 license. Whether to height filter for ~/input/obstacle_pointcloud and ~/input/raw_pointcloud? By default, the height is set to -1~2m. Whether to use the optional obstacle and raw point cloud? If this is true, ~/input/obstacle_pointcloud and ~/input/raw_pointcloud topics will be received. Whether to use the optional obstacle point cloud? If this is true, ~/input/obstacle_pointcloud topics will be received. The overall point cloud used to input the obstacle point cloud Sensor Model for Occupancy Grid Maps Occupancy grid maps provide a discretized representation of an environment where each of the grid cells is classied into two categories: occupied or free. Also, the unobserved cells are time-decayed like the system noise of the Kalman filter (2). A simple robot localization problem with a landmark-based map is illustrated in Figure 2. Using the previous occupancy grid map, update the existence probability using a binary Bayesian filter (1). The gray cells are represented as UNKNOWN cells. In other words, the black points are determined as the ground, and the red point cloud is the points determined as obstacles. The black and red dots represent raw point clouds, and the red dots represent obstacle point clouds. Therefore, the obstacle point cloud and the raw point cloud are used to reflect what is judged to be the ground and what is judged to be an obstacle in the occupancy grid map. As a result, the occupancy grid map is almost an UNKNOWN cell. The reason is that laserscan only uses the most foreground point in the polar coordinate system, so it throws away a lot of information. Optionally, obstacle point clouds and raw point clouds can be received and reflected in the occupancy grid map. ray trace is done by Bresenham's line algorithm. the node take a laserscan and make an occupancy grid map with one frame.The basic idea is to take a 2D laserscan and ray trace it to create a time-series processed occupancy grid map. This package outputs the probability of having an obstacle as occupancy grid map. Laserscan_to_occupancy_grid_map # Purpose # Pointcloud_preprocessor : occupancy_grid_map_outlier_filter (Optional) Future extensions / Unimplemented parts The `freespace planning algorithms` package The `traffic_light_map_based_detector` Package