Enhancing Manual Scan Registration Using Audio Cues

Indoor mapping and modelling requires that acquired data be processed by editing, fusing, formatting the data, amongst other operations. Currently the manual interaction the user has with the point cloud (data) while processing it is visual. Visual interaction does have limitations, however. One way of dealing with these limitations is to augment audio in point cloud processing. Audio augmentation entails associating points of interest in the point cloud with audio objects. In coarse scan registration, reverberation, intensity and frequency audio cues were exploited to help the user estimate depth and occupancy of space of points of interest. Depth estimations were made reliably well when intensity and frequency were both used as depth cues. Coarse changes of depth could be estimated in this manner. The depth between surfaces can therefore be estimated with the aid of the audio objects. Sound reflections of an audio object provided reliable information of the object surroundings in some instances. For a point/area of interest in the point cloud, these reflections can be used to determine the unseen events around that point/area of interest. Other processing techniques could benefit from this while other information is estimated using other audio cues like binaural cues and Head Related Transfer Functions. These other cues could be used in position estimations of audio objects to aid in problems such as indoor navigation problems.

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GEOMETRICAL NETWORK MODEL GENERATION USING POINT CLOUD DATA FOR INDOOR NAVIGATION

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-4-141-2018 ◽

2018 ◽

Vol IV-4 ◽

pp. 141-146 ◽

Cited By ~ 2

Author(s):

M. Nakagawa ◽

R. Nozaki

Keyword(s):

Network Model ◽

Point Cloud ◽

Optimal Path ◽

Three Dimensional ◽

Point Clouds ◽

Indoor Navigation ◽

Model Generation ◽

Indoor Environments ◽

Point Cloud Data ◽

Cloud Data

<p><strong>Abstract.</strong> Three-dimensional indoor navigation requires various functions, such as the shortest path retrieval, obstacle avoidance, and secure path retrieval, for optimal path finding using a geometrical network model. Although the geometrical network model can be prepared manually, the model should be automatically generated using images and point clouds to represent changing indoor environments. Thus, we propose a methodology for generating a geometrical network model for indoor navigation using point clouds through object classification, navigable area estimation, and navigable path estimation. Our proposed methodology was evaluated through experiments using the benchmark of the International Society for Photogrammetry and Remote Sensing for indoor modeling. In our experiments, we confirmed that our methodology can generate a geometrical network model automatically.</p>

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A New Organization and Indexing Method of Multibeam Point Cloud Data in 3D Marine GIS

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.405-408.3053 ◽

2013 ◽

Vol 405-408 ◽

pp. 3053-3056 ◽

Cited By ~ 1

Author(s):

Lei Peng

Keyword(s):

Spatial Data ◽

Point Cloud ◽

Specific Area ◽

Level Of Detail ◽

Visualization Method ◽

Point Cloud Data ◽

Cloud Data ◽

Terrain Model ◽

Area Of Interest ◽

Current Area

Multibeam sounding system can be used for the acquisition of high-precision digital seabed terrain model, it brings new opportunities for the development of 3D marine GIS (Geographic Information System). But its massive point cloud data poses great challenge for its storage and visualization. This paper proposes a new storage strategy and gives an optimized visualization method specifically for the storage strategy. First, we take advantage of the point cloud coordinates regularity to sort these points according to certain rules and efficiently organize it through multiple spatial index. Vertically, a multi-level pyramid index is established. We generate a number of levels data with different level of detail and different scale, namely the static level of detail (LOD) model, in this way, a trade-off between appropriate redundant storage space and efficient data retrieve is achieved. Horizontally, for all levels of the pyramid model, we establish a tiled quadtree index, and generate a cache image for each tile. Secondly, an optimized visualization method in accordance with the storage strategy is also designed. Based on the current area of interest and data dispatch algorithm, we calculate the corresponding tile location and directly load the cache image to lessen the workload of complex spatial data operation, reduce the rendering burden of spatial data. We can generate customized dynamic cache with specific area of interest and scale, to improve the visual effect, too.

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Analysis of Efficient Visualization and Interactive Editing of 3D Point Cloud Data

TECHART Journal of Arts and Imaging Science ◽

10.15323/techart.2016.05.3.2.32 ◽

2016 ◽

Vol 3 (2) ◽

pp. 32

Author(s):

Rekha Patil ◽

Ashok Kumar Patil ◽

Youngho Chai

Keyword(s):

Point Cloud ◽

3D Point Cloud ◽

Point Cloud Data ◽

Cloud Data ◽

Interactive Editing

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Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2021.65.1.01050110.2352/j.imagingsci.technol.2021.65.1.010501 ◽

2020 ◽

Author(s):

Jiayong Yu ◽

Longchen Ma ◽

Maoyi Tian, ◽

Xiushan Lu

Keyword(s):

Point Cloud ◽

Image Data ◽

Point Clouds ◽

Optical Image ◽

Point Cloud Data ◽

Feature Points ◽

Lidar System ◽

Registration Accuracy ◽

Cloud Data ◽

Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

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