Details

Autor(en) / Beteiligte

• Cui, Zhiqiang
• Liao, Zhaoyang
• Lin, Xubin
• Sun, Kezheng
• Cheng, Taobo
• Zhou, Xuefeng

Titel

Multi-View consistency-based point cloud registration method with low overlap rate

Ist Teil von

• Journal of physics. Conference series, 2024-03, Vol.2724 (1), p.12034

Ort / Verlag

Bristol: IOP Publishing

Erscheinungsjahr

2024

Quelle

EZB-FREE-00999 freely available EZB journals

Beschreibungen/Notizen

• Abstract Accurate and efficient workpiece measurement is crucial for workpiece processing and quality monitoring. Non-contact optical measurement methods have gained more attention due to their simplicity, efficiency, and flexibility compared to complicated and inefficient contact measurement methods. Multi-view registration of measurement data is a key issue in workpiece measurement, as it relies on the system’s geometric accuracy and motion stability, presenting challenges such as the insufficient overlap of multi-viewpoint cloud data and cumulative error. To address these challenges, this paper proposes a multi-view planning and registration algorithm with a low overlap rate. The multi-view planning algorithm employs a greedy method to plan the scanning viewpoints of the workpiece to obtain complete point cloud data efficiently. The multi-view registration algorithm extracts features using a multi-scale geometric feature extraction network, matches the features based on the Hungarian algorithm, builds a graph, and optimizes workpiece positions based on the G2O algorithm for multi-view registration, effectively reducing cumulative error. Measurement experiments on blade workpieces confirm the feasibility of the proposed algorithms.