This paper addresses the control of a team of autonomous agents pursuing a smart evader in a non-accurately mapped terrain. By describing the problem as a partial information Markov game, we are able to integrate map-learning and pursuit. We propose receding horizon control policies, in which the pursuers and the evader try to respectively maximize and minimize the probability of capture at the next time instant. Because this probability is conditioned to distinct observations for each team, the resulting game is nonzero-sum. When the evader has access to the pursuers' information, we show that a Nash solution to the one-step nonzero-sum game always exists. Moreover, we propose a method to compute the Nash equilibrium policies by solving an equivalent zero-sum matrix game. A simulation example shows the feasibility of the proposed approach.

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We propose a hybrid visual odometry algorithm to achieve accurate and low-drift state estimation by separately estimating the rotational and translational camera motion. Previous methods usually estimate the six degrees of freedom camera motion jointly without distinction between rotational and translational motion. However, inaccuracy in the rotation estimate is a main source of drift in visual odometry. We design a hybrid visual odometry algorithm which separately estimates the rotational and translational motion to achieve improved accuracy and low drift error. To improve the accuracy of rotational motion estimation, we exploit orthogonal planar structures, such as walls, floors, and ceilings, common in man-made environments. We track orthogonal frames with an efficient SO(3)-constrained mean-shift algorithm, resulting in drift-free rotation estimates. Based on the absolute camera orientation, we newly propose a way to compute the translational motion by minimizing the de-rotated reprojection error with the tracked features. We compare the proposed algorithm with other state-of-the-art visual odometry methods and demonstrate an improved performance and lower drift error.

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