Stochastic Traveling Salesperson Problem with Neighborhoods for Object Detection

TL;DR

This paper introduces a stochastic TSP with neighborhoods for object detection, optimizing routes considering perception and travel costs, and proposes approximation algorithms with proven bounds for disjoint and non-disjoint regions.

Contribution

It formulates a new route planning problem combining detection and travel costs, and proposes novel approximation algorithms with theoretical guarantees for different region types.

Findings

The proposed method achieves efficient trajectories in simulation.

Approximation ratio of O(DmaxDmin) for disjoint regions.

Finite detour method for non-disjoint regions using curvature properties.

Abstract

We introduce a new route-finding problem which considers perception and travel costs simultaneously. Specifically, we consider the problem of finding the shortest tour such that all objects of interest can be detected successfully. To represent a viable detection region for each object, we propose to use an entropy-based viewing score that generates a diameter-bounded region as a viewing neighborhood. We formulate the detection-based trajectory planning problem as a stochastic traveling salesperson problem with neighborhoods and propose a center-visit method that obtains an approximation ratio of O(DmaxDmin) for disjoint regions. For non-disjoint regions, our method -provides a novel finite detour in 3D, which utilizes the region's minimum curvature property. Finally, we show that our method can generate efficient trajectories compared to a baseline method in a photo-realistic simulation environment.