Microcredential
Sensor Fusion Engineer
Mercedes Benz via Udacity Nanodegree
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10
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Overview
Learn to fuse lidar point clouds, radar signatures, and camera images using Kalman Filters to perceive the environment and detect and track vehicles and pedestrians over time.
Syllabus
You should have intermediate C++ knowledge, and be familiar with calculus, probability, and linear algebra. See detailed requirements.
Lidar
Process raw lidar data with filtering, segmentation, and clustering to detect other vehicles on the road.
Lidar Obstacle DetectionRadar
Analyze radar signatures to detect and track objects. Calculate velocity and orientation by correcting for radial velocity distortions, noise, and occlusions.
Radar Obstacle DetectionCameras
Fuse camera images together with lidar point cloud data. You'll extract object features, classify objects, and project the camera image into three dimensions to fuse with lidar data.
Camera and Lidar FusionKalman Filters
Fuse data from multiple sources using Kalman filters, and build extended and unscented Kalman filters for tracking nonlinear movement.
Unscented Kalman Filters
Taught by
David Silver, Stephen Welch, Andreas Haja, Abdullah Zaidi and Aaron Brown
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