Description
Introduction and Setup
In the Simulator, load loopMap and run a control program that will drive the robot into walls (you may use
the function backupBump.m or write a new function). Make sure you are collecting the following sensor data
in dataStore: truthPose, bump and depth. Allow the robot to move around the environment and bump into
most (if not all) walls. Access the sensor data in the MATLAB workspace by typing global dataStore, and
make sure the data was collected properly. You will use this data in the two mapping sections. Remember
that in the simulator, the robot radius is .16, and the sensor origin is [.13 0].
As you have seen, each bump sensor of the Create returns a value of 1 if the sensor is triggered, and 0
otherwise (with no false alarms). Initialize a 25 × 25 occupancy grid of the environment with p0(occ) = 0.5
as the prior occupancy probability for all cells (i.e. `0 = 0). The boundaries of the environment in the x and
y directions are [−2.5, 2.5]. You may find the MATLAB function meshgrid.m helpful.
Mapping – Occupancy Grid with Bump Sensor (45 points)
1. Plot the occupancy grid boundaries and the robot’s trajectory. Indicate with a marker (e.g. star,
triangle) the locations where a bump sensor was triggered.
2. Write a function logOddsBump.m to calculate the log-odds `t(occ) of cells in the grid using only bump
sensor measurements. Make sure you allow non-square grids (n × m, where n is the number of cells
along the X dimension and m the number of cells along the Y dimension). What are the inputs? what
are the outputs?
3. Explain the sensor model you used, assumptions you made in logOddsBump.m, and why they make
sense.
4. Write a function plotOccupancyGrid.m that given the log odds and the grid, plots unoccupied cells in
white, occupied cells in black, and uncertain cells in shades of gray. You may use colormap(flipud(gray)),
pcolor or image to help plot the grid cells. Plot the occupancy grid at three different time steps using
the log odds obtained from datastore.truthPose and datastore.bump.
5. If you had to choose for each cell whether it is occupied or free (for example, to use the map in a
planning algorithm), what would you do? Apply this method to the grid at the last timestep and plot
the result (each cell is assigned either 0 or 1).
6. Repeat (4,5) with a 50 × 50 grid. What do you observe?
7. What differences might you expect to observe between the map generated using simulated bump sensors
and one using real bump sensor data? Why?
Mapping – Occupancy Grid with Depth Information (45 points)
1. Write a function logOddsDepth.m to calculate the log-odds `t(occ) of cells in the grid using only depth
sensor measurements. What are the inputs? what are the outputs?
2. Explain the sensor model you used, assumptions you made in logOddsDepth.m, and why they make
sense.
3. Plot the occupancy grid at three different timesteps using the log odds obtained from datastore.truthPose
and datastore.rsdepth.
4. Repeat (2,3) with a 50 × 50 grid. What do you observe?
5. What are the pros and cons of using a finer grid resolution?
6. Compare the maps generated using depth with those using the bump sensor. Is one map “better” than
the other? Why or why not?
7. What differences might you expect to observe between the map generated using simulated depth sensors
and one using real depth sensor data? Why?
Test function (10 points)
Edit the function TestOccupancyGrid.m that takes as input,
• dataStore
• `0 (scalar value for the prior occupancy probability of grid cells)
• NumCellsX (number of cells in the X axis)
• NumCellsY (number of cells in the Y axis)
• boundaryX (1×2 vector representing the interval of possible values for X, for example [-2.5 2.5])
• boundaryY (1×2 vector representing the interval of possible values for Y, for example [-2.5 2.5])
outputs,
• lFinalBump (final occupancy grid using bump sensor)
• lFinalDepth (final occupancy grid using depth sensor)
and plots final occupancy grids, one for the bump (as in part 4 of the mapping with bump sensor section) and
one for the depth information (as in part 3 of the mapping with depth information section). Plots generated
should have meaningful titles and labels.
Submit this function in the autograded assignment Homework 5 TestOccupancyGrid.m on Canvas. Course
staff will use this function to test your code with different inputs, and we are using the autograder as a way
to ensure that the test function runs. We are NOT testing for correctness of the algorithm. Make sure the
zip file you submit on Canvas contains this function and all of the functions necessary to run it.
2
