Familiarity-dependent computational modelling of indoor landmark selection for route communication: a ranking approach

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

Accurate semantic segmentation of RGB-D images for indoor navigation

We introduce an approach of semantic segmentation to detect various objects for the mobile robot system “ROSWITHA” (RObot System WITH Autonomy). Developing a semantic segmentation method is a challenging research field in machine learning and computer vision. The semantic segmentation approach is robust compared with the other traditional state-of- the-art methods for understanding the surroundings. Semantic segmentation is a method that presents the most information about the object, such as classification and localization of the object on the image level and the pixel level, thus precisely depicting the shape and position of the object in space. In this work, we experimented with verifying the effectiveness of semantic segmentation when used as an aid to improving the performance of robust indoor navigation tasks. To make the output map of semantic segmentation meaningful, and enhance the model accuracy, points cloud data were extracted from the depth camera, which fuses the data origi- nated from RGB and depth stream to improve the speed and accuracy compared with different machine learning algorithms. We compared our modified approach with the state-of-the-art methods and compared the results when trained with the available dataset NYUv2. Moreover, the model was then trained with the customized indoor dataset 1 (three classes) and dataset 2 (seven classes) to achieve a robust classification of the objects in the dynamic environment of Frankfurt University of Applied Sciences laboratories. The model attains a global accuracy of 98.2%, with a mean intersection over union (mIoU) of 90.9% for dataset 1. For dataset 2, the model achieves a global accuracy of 95.6%, with an mIoU of 72%. Furthermore, the evaluations were performed in our indoor scenario.14 página

3D indoor topological modelling based on homotopy continuation

Indoor navigation is important for various applications such as disaster management, building modelling and safety analysis. In the last decade, the indoor environment has been a focus of extensive research that includes the development of indoor data acquisition techniques, three-dimensional (3D) data modelling and indoor navigation. 3D indoor navigation modelling requires a valid 3D geometrical model that can be represented as a cell complex: a model without any gap or intersection such that the two cells, a room and corridor, should perfectly touch each other. This research is to develop a method for 3D topological modelling of an indoor navigation network using a geometrical model of an indoor building environment. To reduce the time and cost of the surveying process, a low-cost non-contact range-based surveying technique was used to acquire indoor building data. This technique is rapid as it requires a shorter time than others, but the results show inconsistencies in the horizontal angles for short distances in indoor environments. The rangefinder was calibrated using the least squares adjustment and a polynomial kernel. A method of combined interval analysis and homotopy continuation was developed to model the uncertainty level and minimize error of the non-contact range-based surveying techniques used in an indoor building environment. Finally, a method of 3D indoor topological building modelling was developed as a base for building models which include 3D geometry, topology and semantic information. The developed methods in this research can locate a low-cost, efficient and affordable procedure for developing a disaster management system in the near-future

Conceptual spatial representations for indoor mobile robots

We present an approach for creating conceptual representations of human-made indoor environments using mobile robots. The concepts refer to spatial and functional properties of typical indoor environments. Following findings in cognitive psychology, our model is composed of layers representing maps at different levels of abstraction. The complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition. The system also incorporates a linguistic framework that actively supports the map acquisition process, and which is used for situated dialogue. Finally, we discuss the capabilities of the integrated system