Working With Lidar Data slides |

Data Validity

  • Sensors generate a flood of data often drifting and intermixed with some invalid readings
  • The Lidar is no different. Beware of invalid data from the lidar.
  • Lidar on our robots will work correctly between a minimum and maximum distance. Beyond those limits the data is junk.
  • It is useful to filter the lidar data to remove INF, and numbers below the minimum or above the maximume

Working with Lidar Data

  • Lidar data is published as a /scan topic with /LaserScan messages.
  • Here is a great video which provides almost everything you need to understand to manipulate the LaserScan data: Reading Laserscan Data
  • 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]”
  • An infinite LIDAR value in a gazebo simulation is inf (infinity), while that on the actual Turtlebot3 is 0. * A 0 reading from Turltebot3 can refer to either infinity or actual 0 (obstacle is right next to Turtlebot3). Check the valid ranges and also remember that the Lidar’s range is not infinite!

LaserScan

  • The LiDAR data comes via a subscription to /scan topic with message type LaserScan.msg. In Python it will be delivered to you as an object of class LaserScan.

  • LaserScan has lots of fields, see if you can understand them. A key one is .ranges/. ranges in a python list structure and can be easily manipulated as any other list. you can break the data down into relevant chunks (front view, rear view, etc). for more information, try googling: python list slicing, list comprehension, list basics, could be a few staring points. Notice that the in ranges is a little noisy. Before you use it, you need to do some filtering. You will find a min_value in the LaserScan. Check each value you get and if it’s less than the min_value or if its equal to inf consider it to be zero.

Filter Node

  • 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.