Point Clouds to Direct Indoor Pedestrian Pathfinding

Balado, Jesús; Díaz-Vilariño, L.; Arias, Pedro; Frías, Ernesto

doi:10.5194/isprs-archives-xlii-2-w13-753-2019

Cited by 6 publications

(5 citation statements)

References 28 publications

(27 reference statements)

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“…These methods work well to restore holes with known sizes but may not work with irregular holes. Another option for planar surfaces such as the road or sidewalk is to use mathematical morphological operations such as dilation and connected components to fill in holes caused by occlusions [16,17]. A more advanced method uses a sequence of steps including wall detection, occlusion labeling, opening detection, and occlusion reconstruction to reconstruct a 3D model of an indoor environment from laser scans with missing data [15].…”

Section: Geometry-based 3d Scene Completionmentioning

confidence: 99%

Point Cloud Scene Completion of Obstructed Building Facades with Generative Adversarial Inpainting

Chen

Kahoush

et al. 2020

Sensors

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Collecting 3D point cloud data of buildings is important for many applications such as urban mapping, renovation, preservation, and energy simulation. However, laser-scanned point clouds are often difficult to analyze, visualize, and interpret due to incompletely scanned building facades caused by numerous sources of defects such as noise, occlusions, and moving objects. Several point cloud scene completion algorithms have been proposed in the literature, but they have been mostly applied to individual objects or small-scale indoor environments and not on large-scale scans of building facades. This paper introduces a method of performing point cloud scene completion of building facades using orthographic projection and generative adversarial inpainting methods. The point cloud is first converted into the 2D structured representation of depth and color images using an orthographic projection approach. Then, a data-driven 2D inpainting approach is used to predict the complete version of the scene, given the incomplete scene in the image domain. The 2D inpainting process is fully automated and uses a customized generative-adversarial network based on Pix2Pix that is trainable end-to-end. The inpainted 2D image is finally converted back into a 3D point cloud using depth remapping. The proposed method is compared against several baseline methods, including geometric methods such as Poisson reconstruction and hole-filling, as well as learning-based methods such as the point completion network (PCN) and TopNet. Performance evaluation is carried out based on the task of reconstructing real-world building facades from partial laser-scanned point clouds. Experimental results using the performance metrics of voxel precision, voxel recall, position error, and color error showed that the proposed method has the best performance overall.

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Section: Geometry-based 3d Scene Completionmentioning

confidence: 99%

Point Cloud Scene Completion of Obstructed Building Facades with Generative Adversarial Inpainting

Chen

Kahoush

et al. 2020

Sensors

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“…Indoor map path planning is a critical task in indoor navigation systems. Balado et al introduced a method for path planning using indoor point clouds instead of semantic BIM data [32]. The path planning algorithm used in this method is Dijkstra's algorithm, which takes longer to determine the fastest path to the destination than the A* algorithm [33].…”

Section: Indoor Map Path Planning Techniquesmentioning

confidence: 99%

INSUS: Indoor Navigation System Using Unity and Smartphone for User Ambulation Assistance

Fajrianti,

Funabiki,

Sukaridhoto

et al. 2023

Information

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Currently, outdoor navigation systems have widely been used around the world on smartphones. They rely on GPS (Global Positioning System). However, indoor navigation systems are still under development due to the complex structure of indoor environments, including multiple floors, many rooms, steps, and elevators. In this paper, we present the design and implementation of the Indoor Navigation System using Unity and Smartphone (INSUS). INSUS shows the arrow of the moving direction on the camera view based on a smartphone’s augmented reality (AR) technology. To trace the user location, it utilizes the Simultaneous Localization and Mapping (SLAM) technique with a gyroscope and a camera in a smartphone to track users’ movements inside a building after initializing the current location by the QR code. Unity is introduced to obtain the 3D information of the target indoor environment for Visual SLAM. The data are stored in the IoT application server called SEMAR for visualizations. We implement a prototype system of INSUS inside buildings in two universities. We found that scanning QR codes with the smartphone perpendicular in angle between 60∘ and 100∘ achieves the highest QR code detection accuracy. We also found that the phone’s tilt angles influence the navigation success rate, with 90∘ to 100∘ tilt angles giving better navigation success compared to lower tilt angles. INSUS also proved to be a robust navigation system, evidenced by near identical navigation success rate results in navigation scenarios with or without disturbance. Furthermore, based on the questionnaire responses from the respondents, it was generally found that INSUS received positive feedback and there is support to improve the system.

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“…To structure the point cloud without loss of precision, many authors prefer the use of graphs. By applying knn search, points can be connected with the nearest neighbor (Balado et al, 2019). Then, point can be considered as nodes and the connections as edges to create de graph.…”

Section: Related Workmentioning

confidence: 99%

Point-Based Morphological Opening With Input Data Retrieval

Balado

Oosterom

Díaz-Vilariño

et al. 2021

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

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Abstract. Mathematical morphology is a technique recently applied directly for point cloud data. Its working principle is based on the removal and addition of points from an auxiliary point cloud that acts as a structuring element. However, in certain applications within a more complex process, these changes to the original data represent an unacceptable loss of information. The aim of this work is to provide a modification of the morphological opening to retain original points and attributes. The proposed amendment involved in the morphological opening: erosion followed by dilatation. In morphological erosion, the new eroded points are retained. In morphological dilation, the structuring element does not add its points directly, but uses the point positions to search through the previously eroded points and retrieve them for the dilated point cloud. The modification was tested on synthetic and real data, showing a correct performance at the morphological level, and preserving the precision of the original points and their attributes. Furthermore, the conservation is shown to be very relevant in two possible applications such as traffic sign segmentation and occluded edge detection.

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Point Clouds to Direct Indoor Pedestrian Pathfinding

Cited by 6 publications

References 28 publications

Point Cloud Scene Completion of Obstructed Building Facades with Generative Adversarial Inpainting

Point Cloud Scene Completion of Obstructed Building Facades with Generative Adversarial Inpainting

INSUS: Indoor Navigation System Using Unity and Smartphone for User Ambulation Assistance

Point-Based Morphological Opening With Input Data Retrieval

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