Cornell University: ECE 4960

Return to main page

Lab 12: Localization on the real robot

Objective

In this lab you will perform localization, using only the update step of the Bayes filter, on your actual robot. The point of the lab is to appreciate the difference between simulation and real-world systems. We will also provide you with the localization code.

Parts Required

• 1 x Fully assembled robot, with Artemis, TOF sensors, and an IMU.

Prelab

Lectures

Consider going through the lectures on Sensor Models, Motion models.

Grid Localization

Please refer Lab 10 and Lab 11.

Localization Module:

We provide you with a fully-functional and optimized Bayes filter implementation that works on the virtual robot. You will make changes in the code so that the module can work with your real robot. Note the time it takes to run the prediction and update step. You will find that we took several measures to enable fast computation in practice:

• Pre-caching ray casting values
• Numpy vector processing
• Skipping grid cells with low probabilities in the prediction step
• Utilizing fixed size numpy arrays instead of variable-length lists
• Minimizing the use of temporary variables

Ground Truth

Since there is no automated method to get the real robot’s ground truth pose; you will need to identify certain known poses. You can measure the distance from the origin (0,0) of the map in terms of the number of (1 feet long) tiles in the x and y directions. For this lab, you will need to place your robot in the 4 marked positions in the map.

Observation Loop

Your robot needs to output ToF sensor readings taken at 20 degree increments, starting from the robot’s heading. You can use both your ToF sensors to collectively output sensor range readings at 20 degree increments (i.e. +0, +20, +40 ,… +340 degrees from the current robot heading). Positive angles are along the counter-clockwise direction.

If you are unable to get 18 sensor readings that are 20 degrees apart, you can change the number of sensor readings per loop (observation_count) in world.yaml. Remember that reducing the number of observations reduces the accuracy of your localized pose.

Setup the base code

1. Copy lab12_sim.ipynb and lab12_real.ipynb from here into the notebooks directory (inside the simulation base code directory).
2. Copy localization_extras.py from here into the root directory (inside the ECE4960-sim-release directory).
3. You will need to copy the necessary Bluetooth python modules (present inside directory ble_python) from your previous labs. Copy ONLY the files “base_ble.py”, “ble.py”, “connection.yaml” and “cmd_types.py” into the notebooks directory.

   Do not copy the utils.py file.

Files Provided:

1. localization_extras.py: Provides a fully-functional Localization module that works on the virtual robot.
2. lab12_sim.ipynb: A Jupyter notebook that demonstrates the Bayes filter implementation on the virtual robot.
3. lab12_real.ipynb: A Jupyter notebook that provides you with a skeleton code to integrate the real robot with the localization code.

Tasks

1. Test Localization in Simulation: Run the notebook lab12_sim.ipynb and attach a single screenshot of the final plot (odom, ground truth and belief).
2. Using a uniform prior on the pose, run (only) the update step using the sensor measurement data to localize your robot
   1. Go through the notebook lab12_real.ipynb and implement the member function perform_observation_loop of class RealRobot (re-use code from previous labs to implement this).
   2. Place your robot in one of the four marked poses and run the update step of the Bayes filter once.
      • How close is the localized pose w.r.t to the ground truth?
      • Visualize your results
      • Discuss your results
   3. Repeat (2) for every marked position.
      • Does the robot localize better in certain poses? If so, why?

Marked Poses

The four marked poses in the lab are:

• (-3 ft ,-2 ft ,0 deg)
• (0 ft,3 ft,0 deg)
• (5 ft,-3 ft,0 deg)
• (5 ft,3 ft,0 deg)

Tips

1. The output of the member function perform_observation_loop() needs to be a numpy column array, as required by the provided localization code. If you have a python list of the 18 sensor readings, use the code snippet below to convert the list to a numpy column array:

   np.array(array)[np.newaxis].T
   

2. If you get poor localization results:
   1. Make sure the robot is performing a counter-clockwise rotation with the first sensor reading taken at the current robot heading.
   2. Adjust the sensor noise in the configuration file (sensor_sigma in sensor_sigma).
3. If you need to call asyncio coroutines (ex: await syncio.sleep(3)) inside a function, you need to add the keyword async to the function definition. Here is an example:

   async def sleep_for_3_secs():
      await asyncio.sleep(3)
   
   await sleep_for_3_secs()
   

   NOTE: Notice the keyword “async” in the function definition. Any function that uses the keyword “await” (i.e. calls ascyncio coroutine), should have the async keyword in the definition. To call the async “function” or coroutine, use the “await” keyword.

4. If you need to use an await inside RealRobot.perform_observation_loop() for some reason (for e.g. you are using BLE handlers to get the observation data from the real robot), you need to make changes to the localization functions which call this function. Here is a walkthrough of the function definitions that need to be modified:
   1. Add the async keyword to the function defintion RealRobot.perform_observation_loop()
   2. Add the async keyword to the function defintion BaseLocalization.get_observation_data() [Ref]
   3. Add the await keyword when calling the async coroutine perform_observation_loop() [Ref]
   4. In the code cell in the Jupyter notebook lab12_real.ipynb, append the await keyword to the line loc.get_observation_data()

   NOTE: You could skip the above steps and instead directly call the asyncio sleep coroutine as asyncio.run(asyncio.sleep(3)) inside the non-async function RealRobot.perform_observation_loop(), however, this may not be the right way to use asyncio coroutines and could pose issues.

Write-up

To demonstrate that you’ve successfully completed the lab, please upload a brief lab report (<1.000 words), with code (not included in the word count), photos, and videos documenting that everything worked and what you did to make it happen. Include the robot’s belief after localization for each pose, compare it with the ground truth pose, and write down your inference.