Online trajectory optimization to improve object recognition

We present an online trajectory optimization approach that optimizes a trajectory such that object recognition performance is improved. Inspired by prior work, we formulate the optimization as a derivative-free stochastic optimization, allowing us to express the cost function in an arbitrary way. The cost function is defined such that information acquisition of target objects is improved, while simultaneously moving towards the goal point. We show the evaluation of our approach on a quadrotor platform in simulation as well as on a real robot. The results show that by using an online optimization approach recognition accuracy is greatly improved, but more importantly the optimized trajectory reduces the uncertainty of the posterior class distribution greatly. Hence, verifying that the optimized trajectory collects more valuable information.

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