Geometrical Network Model Generation Using Point Cloud Data for Indoor Navigation

Nakagawa, Masafumi; Nozaki, Riku

doi:10.5194/isprs-annals-iv-4-141-2018

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…Li et al [54] designed a deep network combining PointNet with the Markov random field to identify single objects from the point cloud more precisely for indoor navigation. Nakagawa and Nozaki [55] proposed a method that included object classification, navigable area estimation, and navigable path estimation to construct a geometrical network model for indoor navigation. Liu et al [56] designed an indoor spatial navigation model to support navigation and logical models from floor plans.…”

Section: (C) Nsmmentioning

confidence: 99%

A Novel Indoor Structure Extraction Based on Dense Point Cloud

Shi

Zeng

2020

IJGI

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Herein, we propose a novel indoor structure extraction (ISE) method that can reconstruct an indoor planar structure with a feature structure map (FSM) and enable indoor robot navigation using a navigation structure map (NSM). To construct the FSM, we first propose a two-staged region growing algorithm to segment the planar feature and to obtain the original planar point cloud. Subsequently, we simplify the planar feature using quadtree segmentation based on cluster fusion. Finally, we perform simple triangulation in the interior and vertex-assignment triangulation in the boundary to accomplish feature reconstruction for the planar structure. The FSM is organized in the form of a mesh model. To construct the NSM, we first propose a novel ground extraction method based on indoor structure analysis under the Manhattan world assumption. It can accurately capture the ground plane in an indoor scene. Subsequently, we establish a passable area map (PAM) within different heights. Finally, a novel-form NSM is established using the original planar point cloud and the PAM. Experiments are performed using three public datasets and one self-collected dataset. The proposed plane segmentation approach is evaluated on two simulation datasets and achieves a recall of approximately 99%, which is 5% higher than that of the traditional plane segmentation method. Furthermore, the triangulation performance of our method compared with the traditional greedy projection triangulation show that our method performs better in terms of feature representation. The experimental results reveal that our ISE method is robust and effective for extracting indoor structures.

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Section: (C) Nsmmentioning

confidence: 99%

A Novel Indoor Structure Extraction Based on Dense Point Cloud

Shi

Zeng

2020

IJGI

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“…Essential requirements for indoor navigation are: a way to localise the person to be navigated (indoor positioning), a map or 3D model of the environment, a network graph representing navigable paths, a path-finding algorithm and a human interpretation of the path (wayfinding) (Nakagawa and Nozaki, 2018). This paper focuses on the third: creating a navigation graph for an indoor environment, in which nodes represent locations, and edges the connectivity between these locations.…”

Section: Indoor Navigation Networkmentioning

confidence: 99%

“…This graph can be used to calculate optimal routes between nodes, in order to navigate people through buildings. Sithole (2018) and Nakagawa and Nozaki (2018) describe three main types of navigation networks found in related work: 1) graphs that link separate indoor spaces (e.g. rooms and corridors), 2) grid structured graphs that are based on small-scale cells or voxels (tessellation), and 3) potential field models, which are continuous and have repelling (obstacles, walls) and attracting (the end goal) forces.…”

Section: Indoor Navigation Networkmentioning

confidence: 99%

“…The latter is most often used in the navigation of autonomous robots and drones (Koren and Borenstein, 1991;Ge and Cui, 2002), and is computationally very heavy for route calculation. Grid structured graphs can be based on 2D cells (pixels), as done by Nakagawa and Nozaki (2018), limited to one floor, or voxels (3D volumetric pixels), as done by Fichtner et al (2018). The last authors create an octree structure in the empty space of a point cloud, and construct a connectivity graph in neighbouring empty octree leaves.…”

Section: Indoor Navigation Networkmentioning

confidence: 99%

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Automatic Extraction of a Navigation Graph Intended for Indoorgml From an Indoor Point Cloud

Flikweert

Peters

Díaz-Vilariño

et al. 2019

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Indoor environments tend to be more complex and more populated when buildings are accessible to the public. The need for knowing where people are, how they can get somewhere or how to reach them in these buildings is thus equally increasing. In this research point clouds are used, obtained by dynamic laser scanning of a building, since we cannot rely on architectural drawings for maps and paths, which can be outdated. The presented method focuses on the creation of an indoor navigation graph, based on IndoorGML structure, in a fast and automated way, while retaining the type of walkable surface. In this paper the focus has been on door detection, because doors are essential elements in an indoor environment, seeing that they connect spaces and are a logical step in a route. This paper describes a way to detect doors using 3D Medial Axis Transform (MAT) combined with the intelligence stored in the path of a mobile laser scanner, showing good first results. Additionally different spaces (e.g. rooms and corridors) in the building are identified and slopes and stairs in walkable spaces are detected. This results in a navigation graph which can be stored in an IndoorGML structure.</p>

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