ABSTRACT

Landmarks play key roles in human wayfinding and mobile navigation systems. Existing computational landmark selection models mainly focus on outdoor environments, and aim to identify suitable landmarks for guiding users who are unfamiliar with a particular environment, and fail to consider familiar users. This study proposes a familiarity-dependent computational method for selecting suitable landmarks for communicating with familiar and unfamiliar users in indoor environments. A series of salience measures are proposed to quantify the characteristics of each indoor landmark candidate, which are then combined in two LambdaMART-based learning-to-rank models for selecting landmarks for familiar and unfamiliar users, respectively. The evaluation with labelled landmark preference data by human participants shows that people’s familiarity with environments matters in the computational modelling of indoor landmark selection for guiding them. The proposed models outperform state-of-the-art models, and achieve hit rates of 0.737 and 0.786 for familiar and unfamiliar users, respectively. Furthermore, semantic relevance of a landmark candidate is the most important measure for the familiar model, while visual intensity is most informative for the unfamiliar model. This study enables the development of human-centered indoor navigation systems that provide familiarity-adaptive landmark-based navigation guidance.

摘要

地标在人类寻路和移动导航系统中发挥着关键作用。现有的计算地标选择模型主要关注户外环境，旨在识别合适的地标，以指导不熟悉特定环境的用户，而未能考虑熟悉的用户。本研究提出了一种熟悉度相关的计算方法，用于选择合适的地标，以便在室内环境中与熟悉和不熟悉的用户进行交流。提出了一系列显着性度量来量化每个室内地标候选者的特征，然后将其组合在两个基于 LambdaMART 的学习排名中分别为熟悉和不熟悉的用户选择地标的模型。人类参与者对标记的地标偏好数据的评估表明，人们对环境的熟悉程度在指导室内地标选择的计算建模中很重要。所提出的模型优于最先进的模型，对熟悉和不熟悉的用户分别达到 0.737 和 0.786 的命中率。此外，地标候选者的语义相关性是熟悉模型最重要的衡量标准，而视觉强度对于不熟悉模型来说是最有用的信息。这项研究能够开发以人为中心的室内导航系统，提供熟悉度自适应的基于地标的导航引导。