予算対応型対応点駆動ビュー選択による移動ロボットのリアルタイム3D知覚
Budget-aware Correspondence-driven View Selection for Real-time 3D Perception of Mobile Robots
ポーズ推定不要でCPU上22FPS以上動作する対応点ベースのビュー選択アルゴリズムBCVSを提案し、冗長な画像列から再構成精度を保ちつつ効率的にフレームを選別できることを示した。
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著者: Vaishnav Ramesh, Jacob Carter, Shivali J. Patel, Sanjeev J. Koppal, Md Jahidul Islam
分類: cs.RO, cs.CV
原文アブストラクト
3D visual perception and scene reconstruction are critical for autonomous robots and intelligent transportation, yet real-world deployments often generate long, redundant image sequences that hinder downstream processing. This report investigates BCVS (budget-aware correspondence-driven view sampling), a pose-free view selection algorithm that offers 22+ FPS on a CPU, enabling online filtering for efficient 3D perception by mobile robots. Unlike existing approaches that rely on full structure-from-motion (SfM) pipelines or camera pose estimation, BCVS employs a purely correspondence-based scoring strategy that prioritizes view novelty while maintaining sufficient overlap for geometric stability. Given a user-specified budget, BCVS iteratively selects the most distinct frames and suppresses near-duplicates, producing compact subsets that preserve connectivity and minimize reconstruction loss. Extensive experiments on benchmark show that BCVS outperforms CPU-friendly baselines at tight budgets and remains competitive as budgets scale. Field trials with large aerial and marine robotics data further validate its practicality and scalability: BCVS reduces redundancy up to 16% while sustaining robust SfM registration and high-quality 3D reconstructions.