Скачать или смотреть 3D Change Detection in PointClouds

In this video, I compare three different approaches for detecting changes in a dynamic warehouse environment using 3D LiDAR data (ROS2 & Open3D). The goal is to accurately identify moved inventory (pallets, boxes) while ignoring static infrastructure (walls, floors).

The 3 Methods Compared:

Method 1: The "naive" approach. It compares scans using purely geometric data.

The Problem: "Ghosting" artifacts appear on walls when objects are moved, creating false positives. It fails to distinguish between actual changes and sensor noise.

Method 2: Geometric Segmentation (RANSAC) A mathematical approach that fits planes to remove the floor and walls.

The Problem: It is "context-blind." While it cleans up the floor, it often accidentally deletes flat objects (like pallets) or fails on uneven ground.

Method 3: Semantic AI (Cylinder3D) - Proposed Solution The advanced approach using Deep Learning to semantically understand the scene.

The Solution: By classifying points into "Buildings" vs. "Objects", we can intelligently filter out the warehouse structure. This solves the ghosting problem and delivers precise, noise-free change detection.

Tools Used: ROS2, Open3D, Cylinder3D (spconv), Python.

#LiDAR #ComputerVision #DeepLearning #ROS2 #Open3D #Robotics #AI #SemanticSegmentation