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Through automated agricultural inspection, farmers can potentially achieve better productivity and accurately predict yields and crop quality. A variety of sensors can be used for agricultural inspection, but the cheapest and most information-rich is the video camera. We collect data in the field from a monocular camera fixed to a mobile inspection platform. For purposes of pineapple crop mapping...
Stereo vision systems in a Robotic navigation environment determine the depth in the form of a depth map image from two or more images which are taken at the same time, but from slightly different viewpoints. Ground suppression from depth map is essential for object reconstruction in 3D environment as the ground surface is considered as traveling medium than as an object. Ground separated depth map...
Traditional indoor 3D structural environment modeling algorithms employ schemes such as clustering of dense point clouds for parameterization and identification of the 3D surfaces. RANSAC based plane fitting is one common approach in this regard. Alternatively, extensions to feature based stereo have also been used, mainly focusing on 3D line descriptions, along with techniques such as half-plane...
As the rapid development of sensing and mapping techniques, it becomes a well-known technology that a map of complex environment can be generated using a robot carrying sensors. However, most of the existing researches represent environments directly using the integration of point clouds or other low-level geometric primitives. It remains an open problem to automatically convert these low-level map...
This paper presents an algorithm for segmenting 3D point clouds. It extends terrain elevation models by incorporating two types of representations: (1) ground representations based on averaging the height in the point cloud, (2) object models based on a voxelisation of the point cloud. The approach is deployed on Riegl data (dense 3D laser data) acquired in a campus type of environment and compared...
In this paper, an algorithm to segment 3D points in dense range maps generated from the fusion of a single optical camera and a multiple emitter/detector laser range finder is presented. The camera image and laser range data are fused using a Markov Random Field to estimate a 3D point corresponding to each image pixel. The textured 3D dense point cloud is segmented based on evidence of a boundary...
This article presents a novel object recognition module which is adapted to the needs in mobile service robotics. It uses information provided from a stereo camera system as pre-processing part of SIFT or SURF. The principle idea is to filter irrelevant information by selecting regions of interest in the disparity map from stereo images and to use the geometrical constraints of the stereo camera system...
This paper describes a novel approach for purely vision based mobile robot navigation. The visual obstacle avoidance and corridor following behavior rely on the segmentation of the traversable floor region in the omnidirectional robocentric view. The image processing employs a supervised approach in which the segmentation optimal with respect to the appearance of the local environment is determined...
Segmenting range data into semantic categories has become a more and more active field of research in robotics. In this paper, we advocate to view this task as a problem of fast, large-scale retrieval. Intuitively, given a dataset of millions of labeled scan points and their neighborhoods, we simply search for similar points in the datasets and use the labels of the retrieved ones to predict the labels...
This paper describes a novel approach to surface fitting for the creation of a 3D surface map for use by a small articulated wall-climbing robot. Both a laser range finder and a low-resolution camera are used to acquire data in a sparse manner. By scanning at large intervals, such as every 5-10°, and then fusing the data, it is shown that it is possible to fit planar surfaces at an accuracy comparable...
In this paper we present an algorithm that allows a human to naturally and easily teach a mobile robot how to recognize objects in its environment. The human selects the object by pointing at it using a laser pointer. The robot recognizes the laser reflections with its cameras and uses this data to generate an initial 2D segmentation of the object. The 3D position of SURF feature points are extracted...
This paper provides a method for indoor semantic mapping in 3D environment. For indoor environment constructed by numerous planar surfaces, plane features are extracted and classified to build the main structure of indoor scene. To identify and cognize different objects located in indoor scene, both the position information and the color information are used in object classification. After the background...
This paper presents a method for obtaining the motion segmentation and 3D localization of multiple mobile robots in an intelligent space. Motion segmentation and positioning are obtained from the images acquired using an array of calibrated and synchronized cameras, without previous knowledge about the number of mobile robots. These cameras are placed in fixed positions within the environment (intelligent...
This paper describes a fast method for segmentation of large-size long-range 3D point clouds that especially lends itself for later classification of objects. Our approach is targeted at high-speed autonomous ground robot mobility, so real-time performance of the segmentation method plays a critical role. This is especially true as segmentation is considered only a necessary preliminary for the more...
This paper dwells upon the promising 3D technology for mobile robots and automation industry. The first part of the paper describes the design details of our own 3D Time of Flight (TOF) scanning system based on 2D laser range finder. The second part presents a specific segmentation technique for 3D outdoor urban environments by the common detection of plane models. In a few words, the technique separates...
Robust feature extraction within 3D environments is a crucial requirement for many autonomous robotic and tracking applications. 3D Laser range finders and cameras provide extremely rich data about an environment. However, the algorithms which attempt to compress the vast data sets produced by these sensors into features, tend to be fragile in the presence of sensor noise, or computationally expensive...
We present an algorithm to model 3D workspace and to understand test scene for navigation or human computer interaction in network-based mobile robot. This was done by line-based modelling and recognition algorithm. Line-based recognition using 3D lines has been tried by many researchers however its reliability still needs improvement due to ambiguity of 3D line feature information from original images...
This paper addresses the problem of map matching for robot navigation. We propose 3D map matching by a modified RANSAC algorithm for line edges acquired by a stereo camera. 3D map-matching has proven to be successful in indoor and outdoor environments.
This paper proposes a real-time scene segmentation method based on stereovision and intended for the use on a home service robot. In the first step of our approach the input disparity image is replaced by a lower resolution image. Its pixel disparity values are the result of building histograms over small neighbourhoods in the original image and selecting the maxima. This significantly reduces noise...
Finding traversable paths using computer vision is one of the most important components of an intelligent mobile robot system. For a wall climbing robot that operates in an urban environment, it is essential to automatically detect surface types and orientations for switching between moving and climbing, and for applying different adhesive forces both to save energy and ensure its own safety. This...
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