匿名の相対計測を用いたビジョンベース複数MAV位置推定：結合確率データ関連付けフィルタ
Vision-based Multi-MAV Localization with Anonymous Relative Measurements Using Coupled Probabilistic Data Association Filter
GPSやモーションキャプチャが使えない環境で、機体のVIOと匿名の視覚的ロボット間検出を融合し、初期配置やデータ関連付けが未知で誤検出・誤差を含む状況でも全機の位置を共通座標系で推定する手法を提案した。
著者: Ty Nguyen, Kartik Mohta, Camillo J. Taylor, Vijay Kumar
分類: cs.RO
原文アブストラクト
We address the localization of robots in a multi-MAV system where external infrastructure like GPS or motion capture systems may not be available. Our approach lends itself to implementation on platforms with several constraints on size, weight, and power (SWaP). Particularly, our framework fuses the onboard VIO with the anonymous, visual-based robot-to-robot detection to estimate all robot poses in one common frame, addressing three main challenges: 1) the initial configuration of the robot team is unknown, 2) the data association between each vision-based detection and robot targets is unknown, and 3) the vision-based detection yields false negatives, false positives, inaccurate, and provides noisy bearing, distance measurements of other robots. Our approach extends the Coupled Probabilistic Data Association Filter (CPDAF)[1] to cope with nonlinear measurements. We demonstrate the superior performance of our approach over a simple VIO-based method in a simulation with the measurement models statistically modeled using the real experimental data. We also show how onboard sensing, estimation, and control can be used for formation flight.
