From Pixels to Maps: Segmentation, Point Clouds, and 2D Costmaps Explained скачать в хорошем качестве

From Pixels to Maps: Segmentation, Point Clouds, and 2D Costmaps Explained

In this video, we walk through a full perception pipeline used in autonomous robots, drones, and vehicles — all inside a simulated environment. You’ll see how raw camera images are transformed step‑by‑step into meaningful spatial understanding. What we cover: Semantic segmentation from the onboard camera Point cloud generation using depth + segmentation How semantic information improves 3D understanding Building a 2D semantic costmap for navigation Why these steps matter for real‑world autonomy This video is part of a larger series on robotics perception, where we explore object detection, segmentation, fused views, depth‑based point clouds, and semantic mapping. If you’re building autonomous systems or just curious how robots interpret the world, you’ll enjoy this one.