脚式ロボット探索2026/9/16arXiv:2609.19460

狭所未知環境における全方向知覚を用いた姿勢認識型脚式ロボット意味的探索

Pose-aware Legged Robot Semantic Exploration with Omnidirectional Perception in Confined Unknown Environments

脚式ロボットの体幹のピッチ・ロールと全方向カメラLiDARを活用し、対象物の表面被覆率を高めつつ探索時間を短縮する姿勢認識型意味的探索システムPOSEを提案した。

詳しい要約

1. どんなもの？

- 閉所未知環境でのsemantic explorationを対象としたPOSEを提案。 - legged robotのbody pitch/rollとomnidirectional camera-LiDAR perceptionを活用。 - 姿勢を考慮したviewpoint samplingとVLM支援のviewpoint pruningを統合。 - 幾何探索とsemantic viewpointをglobal plannerで統合。

2. 先行研究と比べてどこがすごい？

- planar planning baselineと比較し、最終target-surface coverageを8-10ポイント改善。 - 探索時間を17-32%削減。 - 評価したbaseline中で最高のmean object coverage AUCを達成。 - 限られたvertical FOVとstandoff距離の制約をbody tiltingで克服。

3. 技術・手法の肝は？

- pose-aware viewpoint sampling moduleがpartial object mapsから期待coverage gainに基づきbody postureを選択。 - aim-aligned executionで不要なbody reorientationを削減。 - VLM支援のobject-centric viewpoint pruningがpersistent observation historyとBEV mapsを利用し冗長なinspection visitsを削減。 - semantic viewpointsとgeometric exploration viewpointsをglobal exploration plannerで統合。

4. どうやって有効だと検証した？

- シミュレーションでplanar planning baselineと比較し、coverage改善と探索時間削減を検証。 - mean object coverage AUCで最高性能を確認。 - 実世界実験としてmachine shopでomnidirectional camera-LiDAR suiteを搭載したlegged robotを用いて適用可能性を実証。

5. 議論はある？

- body tiltingによるcoverage向上と追加観測・姿勢遷移によるmission time増加のトレードオフを指摘。 - 提案手法がcoverage-efficiency trade-offを改善することを支持。 - コードは将来コミュニティ向けに公開予定。

6. 次に読むべき論文は？

- 要旨で参照/比較されている研究はplanar planning baselineのみ。 - 関連手法としてsemantic exploration、legged robot navigation、VLM-assisted exploration、BEV-based mappingの定番研究が次に読むべき候補。

※ AIが要旨から生成した要約です。正確性は原文をご確認ください。

著者: Xiaoyang Zhan, Shiyu Chen, Kenji Shimada

分類: cs.RO

原文アブストラクト

Semantic exploration in confined environments requires both environment mapping and detailed observation of target objects. For ground robots, limited sensor vertical fields of view and restricted standoff distances can leave upper object surfaces unobserved from planar viewpoints. Body tilting can improve coverage, but additional observations and posture transitions increase mission time. To address this trade-off, we present POSE, a pose-aware semantic exploration system that exploits a legged robot's intrinsic body pitch and roll with omnidirectional camera-LiDAR perception. The proposed pose-aware viewpoint sampling module selects body postures from partial object maps according to expected coverage gain, while aim-aligned execution reduces unnecessary body reorientation. Further, we introduce an object-centric viewpoint pruning strategy assisted by a vision-language model (VLM), which uses persistent observation history and bird's-eye-view (BEV) maps to reduce redundant inspection visits. The resulting semantic viewpoints are combined with geometric exploration viewpoints in a global exploration planner. Simulations show that POSE improves final target-surface coverage by 8-10 percentage points over the planar planning baseline while reducing exploration time by 17-32%, and achieves the highest mean object coverage AUC among the evaluated baselines. Real-world experiments with a legged robot carrying an omnidirectional camera-LiDAR suite in a machine shop further demonstrate the system's applicability. These results support adaptive body-posture planning for improving the coverage-efficiency trade-off in legged robot semantic exploration. We plan to release the code for community benefit in the future.

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