Sensor Fusion Nano-degree

Lidar Obstacle Detection

1️⃣Introduction to LiDAR and Point Clouds

2️⃣Point Cloud Segmentation

3️⃣Clustering Obstacles

4️⃣Working with Real PCD

Project: Stream back multiple pcd files and perform filtering, segmentation, clustering, and bounding box detection. Detect obstacles in single frames, and make the pipeline even more robust by tracking detections over the history of frames. Create associations between detections in frames and use that to track objects.

😫Tracking and Challenge Problem

Camera

1️⃣Introduction

2️⃣Autonomous Vehicles and Computer Vision

3️⃣Engineering a Collision Detection System

4️⃣Tracking Image Features

5️⃣Midterm Project

6️⃣Combining Camera and Lidar

⭐Project: Track an Object in 3D Space

Radar

0️⃣Introduction

1️⃣Radar Principles

2️⃣Range-Doppler Estimation

3️⃣Clutter, CFAR, AoA

4️⃣Clustering and Tracking

Kalman Filters

1️⃣Kalman Filters (Tracking)

2️⃣Lidar and Radar Fusion with Kalman Filters in C++

2️⃣Lidar and Radar Fusion with Kalman Filters in C++ (follow)

3️⃣Unscented Kalman Filters

3️⃣Unscented Kalman Filters (follow)

🧠Additional Resources