意味論を考慮したパーティクルフィルタによる信頼性の高いブドウ園ロボットの自己位置推定
Semantic-Aware Particle Filter for Reliable Vineyard Robot Localisation
ブドウ園で問題となる反復的な畝の形状による位置推定の失敗を解決するため、ブドウの幹や支柱といった安定した物体検出をパーティクルフィルタに組み込み、意味論的な壁で畝の曖昧さを軽減する手法を提案した。
詳しい要約
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著者: Rajitha de Silva, Jonathan Cox, James R. Heselden, Marija Popovic, Cesar Cadena, Riccardo Polvara
分類: cs.RO, cs.CV
原文アブストラクト
Accurate localisation is critical for mobile robots in structured outdoor environments, yet LiDAR-based methods often fail in vineyards due to repetitive row geometry and perceptual aliasing. We propose a semantic particle filter that incorporates stable object-level detections, specifically vine trunks and support poles into the likelihood estimation process. Detected landmarks are projected into a birds eye view and fused with LiDAR scans to generate semantic observations. A key innovation is the use of semantic walls, which connect adjacent landmarks into pseudo-structural constraints that mitigate row aliasing. To maintain global consistency in headland regions where semantics are sparse, we introduce a noisy GPS prior that adaptively supports the filter. Experiments in a real vineyard demonstrate that our approach maintains localisation within the correct row, recovers from deviations where AMCL fails, and outperforms vision-based SLAM methods such as RTAB-Map.
