怠惰な相対ナビゲーション：視覚とGNSSによる屋内・屋外自律走行
Relatively Lazy: Indoor-Outdoor Navigation Using Vision and GNSS
視覚とGNSSを組み合わせ、絶対状態の推定や地図最適化を遅延させることで、教示直後から屋内・屋外3.5kmの経路を滑らかに自律再走行できる手法を実機で検証した。
著者: Benjamin Congram, Timothy D. Barfoot
分類: cs.RO, cs.CV
原文アブストラクト
Visual Teach and Repeat has shown relative navigation is a robust and efficient solution for autonomous vision-based path following in difficult environments. Adding additional absolute sensors such as Global Navigation Satellite Systems (GNSS) has the potential to expand the domain of Visual Teach and Repeat to environments where the ability to visually localize is not guaranteed. Our method of lazy mapping and delaying estimation until a path-tracking error is needed avoids the need to estimate absolute states. As a result, map optimization is not required and paths can be driven immediately after being taught. We validate our approach on a real robot through an experiment in a joint indoor-outdoor environment comprising 3.5km of autonomous route repeating across a variety of lighting conditions. We achieve smooth error signals throughout the runs despite large sections of dropout for each sensor.
