A pseudo-derivative sliding window approach for path mapping suitable for robotics applications
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Lane Finder

Contact: - Joe MacInnes - jmacinnes19@wooster.edu


  • [A Pseudo-Derivative Method for Sliding Window Path Mapping in Robotics-Based Image Processing] Landon Bentley, Joe MacInnes, Rahul Bhadani and Tamal Bose, 2018

The lane_finder class operates on an already inverse-perspective-mapped image featuring minimal noise (i.e. the lanes are the only thing in the image - mostly).

It implements the pseudo-derivative sliding window algorithm described in our paper to find points along the road for an autonomous vehicle to follow. These points are relative to the car's location, but are not adjusted for any global coordinate space (eg GPS).

The algorithm relies on a variety of hyperparameters that are documented in the class's constructor.


Using the class in a ROS pipeline

The lane_finder class must be constructed with a single image and defined hyperparameters. Thus, a new lane_finder object is created for each frame of input. Using default hyperparameter values, this might look as simple as

lf = lane_finder(cv2_image_obj)

Calling the class's pathGen method returns a ROS path message defined in ros_classes.py that contains lane points to follow relative to the car's location in meters.

The program is compatible with Python 2.

Using lane_finder.py as an executable

lane_finder can be executed at the command line and passed a path to a single image to show visualize the algorithm's performance on a single image.

./lane_finder.py /path/to/image.jpg

Interpreting Visualizations

lane_finder has a visualize method that will return the image with information overlayed on top in the following format:

Green Circles → these points are lane points from the expected right and left lanes. These points are used to calculate the points to follow.

Red Circles → these circles represent the seek forward behavior of the algorithm. Each red circle is an iteration of seeking forward, where the algorithm is trying to rediscover the lane.

Pink Circles → these circles are the points along the road to follow and represent the algorithm's best guess at the mlane midpoints.

Copyright notice

Copyright © 2018 Compositional Systems Lab, Department of Electrical and Computer Engineering, The University of Arizona; Arizona Board of Regents All rights reserved. Copyright text and third party software license information.