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Personal work on sensor fusion for self-driving cars. Including Lidar, Camera, Radar, and Fusion techniques.

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Sensor Fusion

Lidar

  • Process raw lidar data with filtering, segmentation, and clustering to detect other vehicles on the road.
  • Implementation Ransac with planar model fitting to segment point clouds.
  • Implementation Euclidean clustering with a KD-Tree to cluster and distinguish vehicles and obstacles.

Camera

  • Fuse camera images together with lidar point cloud data.
  • Extract object features from camera images in order to estimate object motion and orientation.
  • Classify objects from camera images in order to apply a motion model.
  • Project the camera image into three dimensions.
  • Fuse the projection into three dimensions to fuse with lidar data to estimate Time-to-Collision.

Radar

  • Analyze radar signatures to detect and track objects.
  • Calculate velocity and orientation by correcting for radial velocity distortions, noise, and occlusions.
  • Apply thresholds to identify and eliminate false positives.
  • Filter data to track moving objects over time.

Kalman Filter

  • Fuse data from multiple sources using Kalman filters.
  • Merge data together using the prediction-update cycle of Kalman filters, which accurately track object moving along straight lines.
  • Build extended and unscented Kalman filters for tracking nonlinear movement.
  • Unscented Kalman Filter to estimate the state of multiple cars on a highway using noisy lidar and radar measurements.

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Personal work on sensor fusion for self-driving cars. Including Lidar, Camera, Radar, and Fusion techniques.

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