An Efficient Approximation Algorithm for Multi-criteria Indoor Route Planning Queries

TL;DR

This paper introduces a novel, efficient approximation algorithm for multi-criteria indoor route planning that accounts for indoor-specific features and passes through multiple categories, addressing a significant gap in indoor navigation research.

Contribution

The study formally defines the category aware multi-criteria route planning problem for indoor spaces and proposes a novel dominance-based pruning algorithm to solve it efficiently.

Findings

Algorithm is highly efficient in large indoor environments.

Produces high-quality, near-optimal routes.

Outperforms alternative approaches in experiments.

Abstract

A route planning query has many real-world applications and has been studied extensively in outdoor spaces such as road networks or Euclidean space. Despite its many applications in indoor venues (e.g., shopping centres, libraries, airports), almost all existing studies are specifically designed for outdoor spaces and do not take into account unique properties of the indoor spaces such as hallways, stairs, escalators, rooms etc. We identify this research gap and formally define the problem of category aware multi-criteria route planning query, denoted by CAM, which returns the optimal route from an indoor source point to an indoor target point that passes through at least one indoor point from each given category while minimizing the total cost of the route in terms of travel distance and other relevant attributes. We show that CAM query is NP-hard. Based on a novel dominance-based pruning, we propose an efficient algorithm which generates high-quality results. We provide an extensive experimental study conducted on the largest shopping centre in Australia and compare our algorithm with alternative approaches. The experiments demonstrate that our algorithm is highly efficient and produces quality results.