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.
Master key concepts and practical skills through structured learning modules. By completing this curriculum, you'll gain valuable expertise applicable to real-world scenarios.
Learn to detect obstacles in lidar point clouds through clustering and segmentation, apply thresholds and filters to radar data in order to accurately track objects, and augment your perception by projecting camera images into three dimensions and fusing these projections with other sensor data. Combine this sensor data with Kalman filters to perceive the world around a vehicle and track objects over time.
Learn to fuse data from three of the primary sensors that robots use: lidar, camera, and radar.
You should have intermediate C++ knowledge, and be familiar with calculus, probability, and linear algebra. See detailed requirements.
This comprehensive Sensor Fusion Engineer curriculum is designed to take you from foundational concepts to advanced implementation. Each module builds upon the previous, ensuring a structured learning path that maximizes knowledge retention and practical application.
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Course Instructor
Radar Obstacle Detection
Expert instructor with industry experience
Subject Category
Sensor Fusion, Computer Science
Part of our Sensor Fusion, Computer Science curriculum
Course Language
English
All materials in English
This online course offers comprehensive training with expert instruction, practical exercises, and a certificate of completion. Join thousands of students advancing their careers through quality online education.
Comprehensive Program
Professional training from $399
Pricing may vary. Check the course provider for current promotions and exact pricing.
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