8 Sections
8 Lessons
4 Weeks
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Introduction to Autonomous Systems
1
1.1
Autonomy levels, system architecture, and real-world applications.
Perception & Sensor Fusion
1
2.1
Sensors for autonomy, perception pipelines, and sensor fusion basics.
Localization Techniques
1
3.1
Odometry, GPS, IMU, probabilistic localization, and filtering concepts.
Mapping & SLAM Fundamentals
1
4.1
Occupancy grids, feature maps, and SLAM principles.
Path Planning Algorithms
1
5.1
Global vs local planning, graph-based methods, and search algorithms.
Obstacle Avoidance & Motion Planning
1
6.1
Reactive planning, collision avoidance, and safe navigation strategies.
Navigation Stack Integration
1
7.1
Integrating perception, planning, and control (ROS navigation concepts).
Applications & Case Studies
1
8.1
Autonomous robots, self-driving concepts, drones, and real-world deployments.
Autonomous Systems & Navigation
Curriculum
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