# Visual Monitoring for Multiple Points of Interest on a 2.5D Terrain using a UAV with Limited Field-of-View Constraint

## TL;DR

This paper addresses the challenge of visual monitoring of multiple points on a 2.5D terrain using a UAV with limited field-of-view, proposing an approximation algorithm for planning UAV tours.

## Contribution

It introduces a two-phase strategy with a constant-factor approximation algorithm for the TSPN, reducing it to a GTSP and solving via ILP, for efficient UAV monitoring.

## Key findings

- The proposed algorithm provides near-optimal tours in varied scenarios.
- Comparative evaluation shows effectiveness of the ILP and GTSP approaches.
- Preliminary field experiments validate the practical applicability.

## Abstract

Varying terrain conditions and limited field-of-view restricts the visibility of aerial robots while performing visual monitoring operations. In this paper, we study the multi-point monitoring problem on a 2.5D terrain using an unmanned aerial vehicle (UAV) with limited camera field-of-view. This problem is NP-Hard and hence we develop a two phase strategy to compute an approximate tour for the UAV. In the first phase, visibility regions on the flight plane are determined for each point of interest. In the second phase, a tour for the UAV to visit each visibility region is computed by casting the problem as an instance of the Traveling Salesman Problem with Neighbourhoods (TSPN). We design a constant-factor approximation algorithm for the TSPN instance. Further, we reduce the TSPN instance to an instance of the Generalized Traveling Salesman Problem (GTSP) and devise an ILP formulation to solve it. We present a comparative evaluation of solutions computed using a branch-and-cut implementation and an off-the-shelf GTSP tool -- GLNS, while varying the points of interest density, sampling resolution and camera field-of-view. We also show results from preliminary field experiments.