Edge AI & Autonomous Navigation
0 sessions
6 hours each
0 Hrs total
Live Online
-
Participants can interact with the instructor,
ask questions, seek clarification,
and receive immediate feedback. -
You can receive a full refund before
the second session. After attending
the second session, you will not be eligible
for a refund. -
Access to the session that was originally
conducted in real time. -
You can retake the same course you attended.
-
We will provide you with the notes for the course.
-
We will provide you with the homework
assignments for additional practice. -
You can always reach out to the instructor
between sessions or after the course on Slack.
Full price
$
2000
Bundle and save
4-times payment
$
500
The final price will be
calculated by Klarna
calculated by Klarna
Buy now, pay later
$
166
The final price will be
calculated by Affirm
calculated by Affirm
Upcoming Enrollment
November 30th - December 30th
0 sessions 6 hours each
Online, US Eastern Time
Skills You Will Walk Away With
-
1
Distributed Robotics Systems / Networking
- Configuring a multi-machine ROS 2 ARM Pi 5 + NVIDIA Jetson
- DDS discovery
- DDS Quality-of-Service tuning
- Diagnosing which QoS mismatch
- Static IP assignment on a robot LAN
- Multi-host process orchestration
- Headless operations of embedded devices
- NTP/chrony
- Camera streams
-
2
Computer Vision
- OpenCV in Python and API via the port work
- Camera capture pipelines and V4L2 device handling
- Camera calibration
- Color-space conversion
- Image preprocessing for inference
- Object detection inference
- RViz2 image panels
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3
GPU Acceleration & Edge AI
- CUDA programming model — kernels, threads, blocks, grids, warps
- GPU memory hierarchy
- CuPy for GPU-accelerated array computation (drop-in NumPy replacement)
- Numba JIT compilation and custom kernels
- Host↔device memory transfer management
- TensorRT engine building and ONNX model
- Post-training quantization
- Jetson power modes
- NVIDIA Jetson Orin platform and JetPack SDK
- Thermal and power-envelope management
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4
Sensor Fusion
- Time-synchronizing heterogeneous sensor streams
- Handling variable-rate sensors
- TF2 transform trees applied to fusion
- Designing a perception
- Combining vision detections with range/odometry data into a single actionable state
- Reasoning about sensor confidence and disagreement
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5
SLAM & Mapping
- SLAM Toolbox
- Occupancy grid representation
- Costmap generation
- Saving, serializing, and reloading maps
- Localization against a known map (AMCL)
- Loop closure
- Odometry drift measurement
- Diagnosing a map
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6
Path Planning & Autonomous Navigation
- Dijkstra and A* algorithms
- Heuristic design
- Grid-based planning
- Nav2 stack architecture
- Behavior Trees
- Recovery behaviors
- Costmap2D layer configuration
- Local controller tuning (DWB / MPPI)
- Dynamic obstacle avoidance
- ROS 2 action
Schedule & Enrollment
| Date | Time |