Become a Sensor Fusion Engineer course published by Udacity Academy. Learn to combine lidar point clouds, radar signatures, and camera images using Kalman filters to understand the environment and detect and track vehicles and pedestrians over time.
Learn to identify obstacles in lidar point clouds through clustering and segmentation, apply thresholds and filters to radar data to accurately track objects, and create perception by rendering camera images in 3D and combining these predictions with other sensor data. Strengthen yourself.
Combine this sensor data with Kalman filters to understand the world around a vehicle and track objects over time. Processing raw lidar data with filtering, segmentation and clustering to identify other vehicles on the road. Combine camera images with lidar point cloud data. You extract object features, classify objects, and render the camera image in 3D to combine with lidar data. Analyze radar signatures to identify and track objects. Calculation of velocity and direction with correction for radial velocity distortion, noise and occlusion.
What you will learn in the Become a Sensor Fusion Engineer training course:
- Lidar obstacle detection
- Fusion camera and lidar
- Detection of radar obstacles
- Kalman filters
- Odorless Kalman filters
- Publisher: Udacity
- Instructor: David Silver , Stephen Welch , Andreas Haja , Abdullah Zaidi , Aaron Brown
- English language
- Training level: advanced
- Training duration: 12 hours
Become a Sensor Fusion Engineer Course headings
Become a Sensor Fusion Engineer Course prerequisites
Advanced knowledge in any object-oriented programming language, preferably C++
Intermediate Linear Algebra
Basic Linux Command Lines
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