# Large Scale View Planning

In this paper, we model urban environments as a set of surfaces with $k$ distinct surface normals whose viewing cones must be visited by a robot. We model the resulting coverage problem as a novel variant of Traveling Salesman Problem with Neighborhoods (TSPN). The neighborhoods are defined as cones, which constrain the path coverage quality. We present a polynomial time algorithm which admits an approximation factor of $O(\frac{k}{\tan(\alpha)}\max{[L_B,W_B,H_B]})$, where $\alpha$ is the maximum viewing angle, and $L_B,H_B,W_B$ are respectively the length, width, and height of a minimum enclosing box of a city scene $B$. In addition to the analytical upper bounds, we show in simulations that our method outperforms three baseline methods in both trajectory length and run-time. We also demonstrate our method and evaluate the coverage quality of a city containing more than 70 buildings in a photo-realistic rendering software.