Point clouds to direct indoor pedestrian pathfinding

Increase in building complexity can cause difficulties orienting people, especially people with reduced mobility. This work presents a methodology to enable the direct use of indoor point clouds as navigable models for pathfinding. Input point cloud is classified in horizontal and vertical elements according to inclination of each point respect to n neighbour points. Points belonging to the main floor are detected by histogram application. Other floors at different heights and stairs are detected by analysing the proximity to the detected main floor. Then, point cloud regions classified as floor are rasterized to delimit navigable surface and occlusions are corrected by applying morphological operations assuming planarity and taking into account the existence of obstacles. Finally, point cloud of navigable floor is downsampled and structured in a grid. Remaining points are nodes to create navigable indoor graph. The methodology has been tested in two real case studies provided by the ISPRS benchmark on indoor modelling. A pathfinding algorithm is applied to generate routes and to verify the usability of generated graphs. Generated models and routes are coherent with selected motor skills because routes avoid obstacles and can cross areas of non-acquired data. The proposed methodology allows to use point clouds directly as navigation graphs, without an intermediate phase of generating parametric model of surfacesUniversidade de Vigo | Ref. 00VI 131H 641.02Xunta de Galicia | Ref. ED481B 2016/079-0Xunta de Galicia | Ref. ED431C 2016-038Ministerio de Economía, Industria y Competitividad | Ref. TIN2016-77158-C4-2-RMinisterio de Economía, Industria y Competitividad | Ref. RTC-2016-5257-

Bridging the visual gap in VLN via semantically richer instructions

The Visual-and-Language Navigation (VLN) task requires understanding a textual instruction to navigate a natural indoor environment using only visual information. While this is a trivial task for most humans, it is still an open problem for AI models. In this work, we hypothesize that poor use of the visual information available is at the core of the low performance of current models. To support this hypothesis, we provide experimental evidence showing that state-of-the-art models are not severely affected when they receive just limited or even no visual data, indicating a strong overfitting to the textual instructions. To encourage a more suitable use of the visual information, we propose a new data augmentation method that fosters the inclusion of more explicit visual information in the generation of textual navigational instructions. Our main intuition is that current VLN datasets include textual instructions that are intended to inform an expert navigator, such as a human, but not a beginner visual navigational agent, such as a randomly initialized DL model. Specifically, to bridge the visual semantic gap of current VLN datasets, we take advantage of metadata available for the Matterport3D dataset that, among others, includes information about object labels that are present in the scenes. Training a state-of-the-art model with the new set of instructions increase its performance by 8% in terms of success rate on unseen environments, demonstrating the advantages of the proposed data augmentation method.Comment: Accepted in ECCV 2022. Research completed on November 21, 202

A Proposal for Semantic Map Representation and Evaluation

Semantic mapping is the incremental process of “mapping” relevant information of the world (i.e., spatial information, temporal events, agents and actions) to a formal description supported by a reasoning engine. Current research focuses on learning the semantic of environments based on their spatial location, geometry and appearance. Many methods to tackle this problem have been proposed, but the lack of a uniform representation, as well as standard benchmarking suites, prevents their direct comparison. In this paper, we propose a standardization in the representation of semantic maps, by defining an easily extensible formalism to be used on top of metric maps of the environments. Based on this, we describe the procedure to build a dataset (based on real sensor data) for benchmarking semantic mapping techniques, also hypothesizing some possible evaluation metrics. Nevertheless, by providing a tool for the construction of a semantic map ground truth, we aim at the contribution of the scientific community in acquiring data for populating the dataset

Mobile Augmented Reality Applications to Discover New Environments

International audiencealthough man has become sedentary over time, his wish to travel the world remains as strong as ever. The aim of this paper is to show how techniques based on imagery and Augmented Reality (AR) can prove to be of great help when discovering a new urban environment and observing the evolution of the natural environment. The study's support is naturally the Smartphone which in just a few years has become our most familiar device, which we take with us practically everywhere we go in our daily lives

Mobile Augmented Reality Applications to Discover New Environments

Although man has become sedentary over time, his wish to travel the world remains as strong as ever. The aim of this paper is to show how techniques based on imagery and Augmented Reality (AR) can prove to be of great help when discovering a new urban environment and observing the evolution of the natural environment. The study's support is naturally the Smartphone which in just a few years has become our most familiar device, which we take with us practically everywhere we go in our daily lives.Comment: Science and Information Conference 2013, France (2013

Multi-level indoor navigation ontology for high assurance location-based services

© 2017 IEEE. Indoor navigation will become an importantapplication on a smartphone for Location-Based Service (LBS). An indoor navigation system should work under normalcircumstances and during emergencies, such as fires, during abuilding power shut down, alarm, etc. The LBS should be able tohelp users find the best exit route to the outside of the buildingunder all circumstances and with high reliability. In thisresearch, we develop an indoor ontology model for indoornavigation. This ontology model defines the indoor environmentattributes such as location nodes, and connection points. Thelocation nodes with the location information allow navigation inthe indoor environment. Connection points are able to separatethe map zones and the building floors into a 'Map sheet.' Thisontology approach allows the LBS works in both normalcircumstances and emergencies. This model provides a reliableindoor navigation system for LBS