Reading: Read PRR Chapter 7
Reminder: Readings are your responsibility. You will be expected to come to class prepared, having read the material, and ready to participate in the discussion
Welcome
- Review: how it works in ROS (5 minutes)
- All your code (almost) in ROS is a node
- All information (almost) is transferred in the form of messages
Sensors
- Are electronic devices
- Connected to the Robot’s computer
- Generatel data very fast
- They require real-time processing
- Publishing information
What kinds of sensors?
- LIDAR: Rapidly rotates a laser, returns distance to nearest obstacle on each bearing
- Camera: Measures color and brightness of light in a 2D grid
- IMU: Measures accelration in 6 DOF. Sometimes also measures magnetic north
- Motor encoders>: Measures position of wheels as they rotate
- GPS: Uses satelites to measure position on the earth
- And many more types of sensors
All Sensor Data is Noisy
- The data you get from them is generally large arrays of floating point numbers
- Even if nothing is changing, you will see it change a lot
- Signal vs. Noise
- There are many techniques for extracting the signatl
Sensor Data delivered as ROS topics
- What is the “topic” that the device publishes to
- How frequently does it publish (e.g. 10 times per second, once every second)
- And what is the message type
- Often 10 to 60 times per second. Real Time
Acting on the sensor data
- A node subscribes to that topic
- Writes a “handler” which is invoked each time the sensor node publishes
Simulation and IRL are different
- Sensor’s specifications are different (subtly)
- Ranges and what happens when out of range
- Degree of “randomness”
- Exact dimensions of robots, obstacles, etc.
Lidar Refresher
- Rotating laser beam
- “Time of Flight” Sensor
- Key raw data is an array of numbers
- Indexed by the bearing position of the lidar beam at that instance
- Each cell is the distance in meters
Data from Sensors
- Each sensor, each model and brand, has different specifications and characteristics
- Like all other sensors, the data you get from a Lidar is
noisy
- Depending on the software you may have to ‘clean’ the data before you can make sense of it
- Our robots all use a YDLIDAR X4
/scan topic
- Lidar data is published as a
/scan topic with /LaserScan messages
- The most important field that you need to understand is the “ranges”.
- You can access the reading of a specific angle by using “range[index]”, and access a sub-array by using “range[index0:index1]”
Raw sensor data
- To study the Lidar data we can dump out the lidar data into a .csv file format
rostopic echo /scan -w 4 -p -n 50 > ~/scan_data
Lidar data
- As mentioned there are different model Lidars as well as the simulated Lidar in Gazebo
- Here are some of the facts that are required
- Some of them are present in the
/scan message, others are not
- Lets look at the data: see cosi119_src/data for a short program and the data it generates
- A simple way to look at this data in this: google sheet
Present in the /scan message
- How many “cells” are present in each frame
- What is the minimum and maximum bearing
- (From that you can compute how many radians each “cell” corresponds to)
Not present in the message
- Height of the Lidar from the floor
- Direction it is pointing
- Clockwise vs. counter clockwise
- (The last three are captured as the “coordinate system” of the Lidar or the
tf)
- Unit of distance (almost always Meters)
- Closest and furthest valid distance
- “Invalid” value (0 or NaN or ?)
Python
- Subscribe to
/scan topic
- Message type
LaserScan.msg
- In Python it will be delivered to you as an object of class
LaserScan.
- Consider writing a separate filter node which takes “raw” LiDAR data (subscribes to /scan) and publishes “cleaned up” LiDAR data (publishes for example to /scan/clean).
- A related approach is to subscribe to /scan and publish something like /scan/semantic that instead of just cleaning up the data also computes some mins and maxes in different directions of interest.
Video Tutorials
- Watch and study these for a review from a different perspective.
Thank you. Questions?
(random Image from picsum.photos)