“Turn Left after the WC, and Use the Lift to Go to the 2nd Floor”—Generation of Landmark-Based Route Instructions for Indoor Navigation

Irene Fellner,Georg Gartner,Haosheng Huang

doi:10.3390/ijgi6060183

Abstract

People in unfamiliar environments often need navigation guidance to reach a destination. Research has found that compared to outdoors, people tend to lose orientation much more easily within complex buildings, such as university buildings and hospitals. This paper proposes a category-based method to generate landmark-based route instructions to support people’s wayfinding activities in unfamiliar indoor environments. Compared to other methods relying on detailed instance-level data about the visual, semantic, and structural characteristics of individual spatial objects, the proposed method relies on commonly available data about categories of spatial objects, which exist in most indoor spatial databases. With this, instructions like “Turn right after the second door, and use the elevator to go to the second floor” can be generated for indoor navigation. A case study with a university campus shows that the method is feasible in generating landmark-based route instructions for indoor navigation. More importantly, compared to metric-based instructions (i.e., the benchmark for indoor navigation), the generated landmark-based instructions can help users to unambiguously identify the correct decision point where a change of direction is needed, as well as offer information for the users to confirm that they are on the right way to the destination.

Highlights

In daily life, people often encounter navigation problems when visiting a new place, e.g., “what’s the way from the train station to the city hall”
Turn right after the lounge ‘D2.0.005’” helps the user to confirm he/she is on the right way, as well as identify the decision points (DPs) where he/she needs to turn
Instead of relying on detailed instance-level data about the visual, semantic, and structural characteristics of individual spatial features, the proposed Indoor Landmark Navigation Model (ILNM) relies on commonly available data about categories of spatial features, which can be derived from most existing indoor spatial databases

Summary

Introduction

People often encounter navigation problems when visiting a new place, e.g., “what’s the way from the train station to the city hall”. Outdoor navigation has been a research focus since the first location-based services. After arriving at a destination by using outdoor navigation systems, people often need to enter the buildings and start indoor navigation. Research has shown that compared to outdoors, people tend to lose orientation a lot easier within buildings, especially complex ones, such as hospitals, university buildings, big shopping malls, and airports [1,2]. Different techniques can be used for communicating route/navigational information (directions) in indoor navigation systems, such as maps, verbal instructions (voice-based), augmented reality, and haptic [3]. Literature has shown that to improve navigation performance and provide good user experiences during navigation, landmarks should be included in route instructions, mainly due to their essential roles in human orientation and wayfinding [5,6]

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Discussion

Conclusion