MyRoboPath
Advanced
6 Months
$120,000 - $175,000 / year

Computer Vision & Perception Engineer Roadmap

Give robots eyes: 3D point cloud processing, real-time object detection with YOLO, and Visual Odometry.

Master 2D and 3D computer vision for robotics: OpenCV pipeline optimization, camera intrinsic/extrinsic calibration, stereo vision, depth sensors (RealSense), Point Cloud Library (PCL), YOLOv8 object detection with TensorRT, and AprilTag fiducial tracking.

Target Roles:
Robotics Perception EngineerComputer Vision ResearcherSpatial AI Specialist
Prerequisites:
Proficiency in Python and C++Linear algebra and projective geometryFundamentals of deep learning

Roadmap Curriculum & Milestones

Complete each sequential phase to build production-grade robotics competencies.

3 Major Phases
01

Phase 1: Classical Vision & Camera Calibration

Understand pinhole camera models, lens distortion coefficients, stereo triangulation, and OpenCV acceleration.

Step 1

Pinhole Camera Model, Intrinsic Calibration & AprilTags

Duration: 3 Weeks

Calibrate cameras using chessboard patterns, rectify lens barrel distortion, and calculate 6-DOF pose of AprilTags and ArUco markers.

Core Competencies:
  • Intrinsic & Extrinsic Parameters
  • Radial/Tangential Distortion Rectification
  • Perspective-n-Point (PnP) Solver
  • Fiducial Marker Tracking
Hands-On Projects:
  • Visual servoing tracking node maintaining fixed camera distance to an AprilTag
Tools:OpenCV 4.xapriltag_rosimage_geometry
02

Phase 2: 3D Vision & Point Cloud Processing

Work with RGB-D depth cameras (Intel RealSense, OAK-D) and Point Cloud Library (PCL).

Step 1

Point Cloud Filtering, RANSAC Plane Segmentation & ICP

Duration: 4 Weeks

Filter voxel grids, remove statistical outliers, segment tabletop planes using RANSAC, and align point clouds with Iterative Closest Point (ICP).

Core Competencies:
  • VoxelGrid Downsampling
  • RANSAC Plane Fitting
  • Euclidean Cluster Extraction
  • Iterative Closest Point (ICP) Registration
Hands-On Projects:
  • Tabletop 3D object segmentation and bounding box publisher for robotic grasping
Tools:PCL (Point Cloud Library)Intel RealSense SDKsensor_msgs/PointCloud2
03

Phase 3: Deep Learning & Edge AI on NVIDIA Jetson

Deploy real-time neural networks (YOLOv8, SegFormer) optimized with NVIDIA TensorRT on edge robotics platforms.

Step 1

Real-Time Object Detection with YOLOv8 & TensorRT

Duration: 4 Weeks

Train custom object detection models, convert ONNX weights to TensorRT INT8 engines, and run inference at 60+ FPS on Jetson Orin.

Core Competencies:
  • Custom Dataset Annotation
  • PyTorch to ONNX Export
  • TensorRT Engine Optimization
  • Zero-Copy Jetson Memory Management
Hands-On Projects:
  • Real-time 3D object detection combining 2D YOLO bounding boxes with RealSense depth
Tools:Ultralytics YOLOv8NVIDIA TensorRTJetson Orin NanoCUDA

Ready to begin Phase 1?

Dive into our free hands-on tutorials and build your first physical prototype.

Explore Tutorials