Data acquiring support system using recommendation degree map for 3D outdoor modeling

Asai, Toshihiro; Kanbara, Masayuki; Yokoya, Naokazu

doi:10.1117/12.704292

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…It is very time-consuming to construct detailed models of large complex sites using a manual process. Therefore, in tasks of modeling unstructured environments (Craciun et al 2008), especially in a wide outdoor area, perception planning is required to reduce unobserved portions (Asai et al 2007). One of the main drawbacks is determining how to guide the robot and where to place the sensor to obtain complete coverage of a site (Reed and Allen 2000; Blaer and Allen 2009).…”

Section: Site Modelingmentioning

confidence: 99%

Active vision in robotic systems: A survey of recent developments

Chen

Kwok

2011

The International Journal of Robotics Research

368

204

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In this paper we provide a broad survey of developments in active vision in robotic applications over the last 15 years. With increasing demand for robotic automation, research in this area has received much attention. Among the many factors that can be attributed to a high-performance robotic system, the planned sensing or acquisition of perceptions on the operating environment is a crucial component. The aim of sensor planning is to determine the pose and settings of vision sensors for undertaking a vision-based task that usually requires obtaining multiple views of the object to be manipulated. Planning for robot vision is a complex problem for an active system due to its sensing uncertainty and environmental uncertainty. This paper describes such problems arising from many applications, e.g. object recognition and modeling, site reconstruction and inspection, surveillance, tracking and search, as well as robotic manipulation and assembly, localization and mapping, navigation and exploration. A bundle of solutions and methods have been proposed to solve these problems in the past. They are summarized in this review while enabling readers to easily refer solution methods for practical applications. Representative contributions, their evaluations, analyses, and future research trends are also addressed in an abstract level.

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Section: Site Modelingmentioning

confidence: 99%

Active vision in robotic systems: A survey of recent developments

Chen

Kwok

2011

The International Journal of Robotics Research

368

204

View full text Add to dashboard Cite

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“…However, a lot of images are required to construct a high-quality 3D model. On the other hand, 3D modeling methods based on measurements from several different sensors have also been developed [4][5] [6][7] [8]. One of the advantages of such sensor-based methods is the accuracy of the measurement.…”

Section: Introductionmentioning

confidence: 99%

Detection of 3D points on moving objects from point cloud data for 3D modeling of outdoor environments

Kanatani

Kume

Taketomi

et al. 2013

2013 IEEE International Conference on Image Processing

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A 3D modeling technique for an urban environment can be applied to several applications such as landscape simulations, navigational systems, and mixed reality systems. In this field, the target environment is first measured using several types of sensors (laser rangefinders, cameras, GPS sensors, and gyroscopes). A 3D model of the environment is then constructed based on the results of the 3D measurements. In this 3D modeling process, 3D points that exist on moving objects become obstacles or outliers to enable the construction of an accurate 3D model. To solve this problem, we propose a method for detecting 3D points on moving objects from 3D point cloud data based on photometric consistency and knowledge of the road environment. In our method, 3D points on moving objects are detected based on luminance variations obtained by projecting 3D points onto omnidirectional images. After detecting 3D the points based on evaluation value, the points are detected using prior information of the road environment. Index Terms-Outdoor Environment, 3D Modeling, 3D Point Cloud Data, Moving Objects, HSV Model

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Moving object detection from a point cloud using photometric and depth consistencies

Atsushi

Takehara

Kawai

et al. 2016

2016 23rd International Conference on Pattern Recognition (ICPR)

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Abstract-3D models of outdoor environments have been used for several applications such as a virtual earth system and a vision-based vehicle safety system. 3D data for constructing such 3D models are often measured by an on-vehicle system equipped with laser rangefinders, cameras, and GPS/IMU. However, 3D data of moving objects on streets lead to inaccurate 3D models when modeling outdoor environments. To solve this problem, this paper proposes a moving object detection method for point clouds by minimizing an energy function based on photometric and depth consistencies assuming that input data consist of synchronized point clouds, images, and camera poses from a single sequence captured with a moving on-vehicle system.

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Data acquiring support system using recommendation degree map for 3D outdoor modeling

Cited by 5 publications

References 9 publications

Active vision in robotic systems: A survey of recent developments

Active vision in robotic systems: A survey of recent developments

Detection of 3D points on moving objects from point cloud data for 3D modeling of outdoor environments

Moving object detection from a point cloud using photometric and depth consistencies

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