ETHcavation: 動的建設環境におけるパノプティックシーン理解と物体追跡のためのデータセットとパイプライン
ETHcavation: A Dataset and Pipeline for Panoptic Scene Understanding and Object Tracking in Dynamic Construction Environments
建設現場向けに2Dパノプティックセグメンテーションと3D LiDARマッピングを統合し、カルマンフィルタによる動的物体追跡と少数サンプルでのファインチューニング手法を提案。502枚の手動ラベル付きデータセットと動的パノプティックマッピング技術を公開した。
著者: Lorenzo Terenzi, Julian Nubert, Pol Eyschen, Pascal Roth, Simin Fei, Edo Jelavic, Marco Hutter
分類: cs.RO
原文アブストラクト
Construction sites are challenging environments for autonomous systems due to their unstructured nature and the presence of dynamic actors, such as workers and machinery. This work presents a comprehensive panoptic scene understanding solution designed to handle the complexities of such environments by integrating 2D panoptic segmentation with 3D LiDAR mapping. Our system generates detailed environmental representations in real-time by combining semantic and geometric data, supported by Kalman Filter-based tracking for dynamic object detection. We introduce a fine-tuning method that adapts large pre-trained panoptic segmentation models for construction site applications using a limited number of domain-specific samples. For this use case, we release a first-of-its-kind dataset of 502 hand-labeled sample images with panoptic annotations from construction sites. In addition, we propose a dynamic panoptic mapping technique that enhances scene understanding in unstructured environments. As a case study, we demonstrate the system's application for autonomous navigation, utilizing real-time RRT* for reactive path planning in dynamic scenarios. The dataset (https://leggedrobotics.github.io/panoptic-scene-understanding.github.io/) and code (https://github.com/leggedrobotics/rsl_panoptic_mapping) for training and deployment are publicly available to support future research.
