1. A Robust Method for Lane Tracking Using RANSAC James Ian Vaughn Daniel Gicklhorn CS664 Computer Vision Cornell University Spring 2008

2. Original Project Plan Break into slices to both allow for non- linear solutions as well as reducing time for line fitting. Detect predominant edge marker or road edge if no markers present Algorithm must be robust enough to handle when markings are not present or badly defined. Project Plan

3. Road Model Assumptions Road edges are strong Road edges have a gradient similar to the model edge Road edges are consistent between frames Roads consist of several segments of approximately linear edge points Actual edges have a lot of inlier points Model Assumptions

4. RANSAC as a Line Fitting Algorithm Fast efficient line fitting Dynamic selection of candidate lines with tunable error constraint Able to incorporate spatial priors and progressive model fitting into constraint. Can detect predominant road edges along side if lane markers aren’t present. RANSAC

5. Implementation Using RANSAC, we can incorporate a progressive spatial prior into the error function. Each frame is broken in to road “slices”, consistent, horizontal sections most likely to contain road markings. This reduces the amount of time needed for RANSAC as well as producing a non-linear output Implementation

6. Candidate Error Criteria The RANAC error function is the heart of our method. The error considers both local and priori models to locate “good” line candidates in the scene Lines are restricted by slope, distance from a priori “model” line, and by the edge intensity, density, and gradient angle of their potential “inlier” members. RANSAC Error Model

7. Candidate Error Criteria RANSAC Error Model Error is a linear combination of: Scaled distance of candidate line to model line Angular difference between candidate and model lines. Number of consensus points to a given candidate. Gradient angle difference between local points to a candidate line and the model line. Density of consensus points to a candidate line. Gradient magnitudes of local points to a candidate line.

8. Candidate Error Criteria RANSAC Error Model Error is a linear combination of: Scaled distance of candidate line to model line - Maintains spatial / temporal consistency. Angular difference between candidate and model lines. - Maintains spatial /temporal angular consistency. Number of consensus points to a given candidate. - Looks for best probability of a good line. Gradient angle difference between local points to a candidate line and the model line. - Looks for consistent gradients to model line. Density of consensus points to a candidate line. - Prefers denser line edges to lines. Gradient magnitudes of local points to a candidate line. - Prefers most pronounced lines.

9. Results Simulation Results Results show excellent marking detection and outlier tolerance. With proper calibration and weighting, model performs well. Outputs show need for temporal filtering, but outliers are within tolerance levels.

10. Results Videos

11. Conclusions Conclusions and Future work RANSAC is an easy to constrain / almost modular method for line detection and does well in scaling to the scene where others can fail. Future work to improve this model would include Better optical flow Robust calibration Optimal parameter tuning Temporal Filtering Vehicle Sensor Integration