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')