buildMap
Build occupancy map from lidar scans
Description
map = buildMap(scans,poses,mapResolution,maxRange)occupancyMap map by inserting lidar
scans at the given poses. Specify the
resolution of the resulting map, mapResolution, and the maximum
range of the lidar sensor, maxRange.
Examples
Build Occupancy Map from Lidar Scans and Poses
The buildMap function takes in lidar scan readings and associated poses to build an occupancy grid as lidarScan objects and associated [x y theta] poses to build an occupancyMap.
Load scan and pose estimates collected from sensors on a robot in a parking garage. The data collected is correlated using a lidarSLAM algorithm, which performs scan matching to associate scans and adjust poses over the full robot trajectory. Check to make sure scans and poses are the same length.
load scansAndPoses.mat
length(scans) == length(poses)ans = logical
1
Build the map. Specify the scans and poses in the buildMap function and include the desired map resolution (10 cells per meter) and the max range of the lidar (19.2 meters). Each scan is added at the associated poses and probability values in the occupancy grid are updated.
occMap = buildMap(scans,poses,10,19.2);
figure
show(occMap)
title('Occupancy Map of Garage')
Perform SLAM Using Lidar Scans
Use a lidarSLAM object to iteratively add and compare lidar scans and build an optimized pose graph of the robot trajectory. To get an occupancy map from the associated poses and scans, use the buildMap function.
Load Data and Set Up SLAM Algorithm
Load a cell array of lidarScan objects. The lidar scans were collected in a parking garage on a Husky® robot from ClearPath Robotics®. Typically, lidar scans are taken at a high frequency and each scan is not needed for SLAM. Therefore, down sample the scans by selecting only every 40th scan.
load garage_fl1_southend.mat scans scans = scans(1:40:end);
To set up the SLAM algorithm, specify the lidar range, map resolution, loop closure threshold, and search radius. Tune these parameters for your specific robot and environment. Create the lidarSLAM object with these parameters.
maxRange = 19.2; % meters resolution = 10; % cells per meter slamObj = lidarSLAM(resolution,maxRange); slamObj.LoopClosureThreshold = 360; slamObj.LoopClosureSearchRadius = 8;
Add Scans Iteratively
Using a for loop, add scans to the SLAM object. The object uses scan matching to compare each added scan to previously added ones. To improve the map, the object optimizes the pose graph whenever it detects a loop closure. Every 10 scans, display the stored poses and scans.
for i = 1:numel(scans) addScan(slamObj,scans{i}); if rem(i,10) == 0 show(slamObj); end end
View Occupancy Map
After adding all the scans to the SLAM object, build an occupancyMap map by calling buildMap with the scans and poses. Use the same map resolution and max range you used with the SLAM object.
[scansSLAM,poses] = scansAndPoses(slamObj);
occMap = buildMap(scansSLAM,poses,resolution,maxRange);
figure
show(occMap)
title('Occupancy Map of Garage')