ローカリゼーション不要の教示・再帰ナビゲーション：収束定理に基づく信頼性の高い手法
Navigation without localisation: reliable teach and repeat based on the convergence theorem
移動ロボットが教示済み経路を再走行する際、明示的な自己位置推定を行わずカメラ情報で進行方向のみを補正すれば位置誤差が発散しないことを数学的に示し、計算効率の良い単眼教示・再帰ナビゲーション手法を提案・実験検証した論文。
著者: Tomas Krajnik, Filip Majer, Lucie Halodova, Tomas Vintr
分類: cs.RO
原文アブストラクト
We present a novel concept for teach-and-repeat visual navigation. The proposed concept is based on a mathematical model, which indicates that in teach-and-repeat navigation scenarios, mobile robots do not need to perform explicit localisation. Rather than that, a mobile robot which repeats a previously taught path can simply `replay' the learned velocities, while using its camera information only to correct its heading relative to the intended path. To support our claim, we establish a position error model of a robot, which traverses a taught path by only correcting its heading. Then, we outline a mathematical proof which shows that this position error does not diverge over time. Based on the insights from the model, we present a simple monocular teach-and-repeat navigation method. The method is computationally efficient, it does not require camera calibration, and it can learn and autonomously traverse arbitrarily-shaped paths. In a series of experiments, we demonstrate that the method can reliably guide mobile robots in realistic indoor and outdoor conditions, and can cope with imperfect odometry, landmark deficiency, illumination variations and naturally-occurring environment changes. Furthermore, we provide the navigation system and the datasets gathered at http://www.github.com/gestom/stroll_bearnav.
