scholarly journals PREDICTION OF BUILDING FLOORPLANS USING LOGICAL AND STOCHASTIC REASONING BASED ON SPARSE OBSERVATIONS

2016 ◽  
Vol IV-2/W1 ◽  
pp. 265-270 ◽  
Author(s):  
S. Loch-Dehbi ◽  
Y. Dehbi ◽  
G. Gröger ◽  
L. Plümer
Keyword(s):  
A Priori ◽  
Gaussian Mixture Models ◽  
Point Clouds ◽  
Gaussian Mixture ◽  
Constraint Propagation ◽  
Linear Constraints ◽  
Functional Dependencies ◽  
3D Point Clouds ◽  
Location Parameters ◽  
Novel Method

This paper introduces a novel method for the automatic derivation of building floorplans and indoor models. Our approach is based on a logical and stochastic reasoning using sparse observations such as building room areas. No further sensor observations like 3D point clouds are needed. Our method benefits from an extensive prior knowledge of functional dependencies and probability density functions of shape and location parameters of rooms depending on their functional use. The determination of posterior beliefs is performed using Bayesian Networks. Stochastic reasoning is complex since the problem is characterized by a mixture of discrete and continuous parameters that are in turn correlated by non-linear constraints. To cope with this kind of complexity, the proposed reasoner combines statistical methods with constraint propagation. It generates a limited number of hypotheses in a model-based top-down approach. It predicts floorplans based on a-priori localised windows. The use of Gaussian mixture models, constraint solvers and stochastic models helps to cope with the a-priori infinite space of the possible floorplan instantiations.

A novel method for finding grasping handles in a clutter using RGBD Gaussian mixture models

Robotica ◽  
2021 ◽  
pp. 1-17
Author(s):  
Olyvia Kundu ◽  
Samrat Dutta ◽  
Swagat Kumar
Keyword(s):  
A Priori ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Target Object ◽  
Time Performance ◽  
Pose Detection ◽  
Geometrical Constraints ◽  
Novel Method ◽  
Network Object ◽  
Curvature Information

Abstract The paper proposes a novel method to detect graspable handles for picking objects from a confined and cluttered space, such as the bins of a rack in a retail warehouse. The proposed method combines color and depth curvature information to create a Gaussian mixture model that can segment the target object from its background and imposes the geometrical constraints of a two-finger gripper to localize the graspable regions. This helps in overcoming the limitations of a poorly trained deep network object detector and provides a simple and efficient method for grasp pose detection that does not require a priori knowledge about object geometry and can be implemented online with near real-time performance. The efficacy of the proposed approach is demonstrated through simulation as well as real-world experiment.

scholarly journals USING MOBILE LASER SCANNING DATA FOR FEATURES EXTRACTION OF HIGH ACCURACY DRIVING MAPS

2016 ◽  
Vol XLI-B3 ◽  
pp. 433-439 ◽  
Author(s):  
Bisheng Yang ◽  
Yuan Liu ◽  
Fuxun Liang ◽  
Zhen Dong
Keyword(s):  
Laser Scanning ◽  
Human Error ◽  
Traffic Accidents ◽  
Three Dimensional ◽  
Point Clouds ◽  
High Accuracy ◽  
3D Point Clouds ◽  
Assistant Systems ◽  
Novel Method ◽  
High Flexibility

High Accuracy Driving Maps (HADMs) are the core component of Intelligent Drive Assistant Systems (IDAS), which can effectively reduce the traffic accidents due to human error and provide more comfortable driving experiences. Vehicle-based mobile laser scanning (MLS) systems provide an efficient solution to rapidly capture three-dimensional (3D) point clouds of road environments with high flexibility and precision. This paper proposes a novel method to extract road features (e.g., road surfaces, road boundaries, road markings, buildings, guardrails, street lamps, traffic signs, roadside-trees, power lines, vehicles and so on) for HADMs in highway environment. Quantitative evaluations show that the proposed algorithm attains an average precision and recall in terms of 90.6% and 91.2% in extracting road features. Results demonstrate the efficiencies and feasibilities of the proposed method for extraction of road features for HADMs.

A novel method for extracting skeleton of fruit tree from 3D point clouds

2020 ◽  
pp. 2050051
Author(s):  
Shenglian lu ◽  
Guo Li ◽  
Jian Wang
Keyword(s):  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Fruit Trees ◽  
Fruit Tree ◽  
Tree Canopy ◽  
3D Point Clouds ◽  
Novel Method ◽  
Manual Interaction ◽  
Traditional Approaches

Tree skeleton could be useful to agronomy researchers because the skeleton describes the shape and topological structure of a tree. The phenomenon of organs’ mutual occlusion in fruit tree canopy is usually very serious, this should result in a large amount of data missing in directed laser scanning 3D point clouds from a fruit tree. However, traditional approaches can be ineffective and problematic in extracting the tree skeleton correctly when the tree point clouds contain occlusions and missing points. To overcome this limitation, we present a method for accurate and fast extracting the skeleton of fruit tree from laser scanner measured 3D point clouds. The proposed method selects the start point and endpoint of a branch from the point clouds by user’s manual interaction, then a backward searching is used to find a path from the 3D point cloud with a radius parameter as a restriction. The experimental results in several kinds of fruit trees demonstrate that our method can extract the skeleton of a leafy fruit tree with highly accuracy.

scholarly journals A 2.5D APPROACH TO SKIN WRINKLES SEGMENTATION

2019 ◽  
Vol 38 (1) ◽  
pp. 75 ◽  
Author(s):  
Etienne Decencière ◽  
Amira Belhedi ◽  
Serge Koudoro ◽  
Frédéric Flament ◽  
Ghislain François ◽  
...  
Keyword(s):  
Point Clouds ◽  
Skin Surface ◽  
Fringe Projection ◽  
Physical Parameters ◽  
Projection System ◽  
Beneficial Effects ◽  
3D Point Clouds ◽  
Novel Method ◽  
Skin Wrinkles ◽  
Skin Wrinkling

Wrinkles or creases are common structures on surfaces. Their detection is often challenging, and can be an important step for many different applications. For instance, skin wrinkle segmentation is a crucial step for quantifying changes in skin wrinkling and assessing the beneficial effects of dermatological and cosmetic anti-ageing treatments. A 2.5D approach is proposed in this paper to segment individual wrinkles on facial skin surface described by 3D point clouds. The method, based on mathematical morphology, only needs a few physical parameters as input, namely the maximum wrinkle width, the minimum wrinkle length, and the minimum wrinkle depth. It has been applied to data acquired from eye wrinkles using a fringe projection system. An accurate evaluation was made possible thanks to manual annotations provided by three different experts. Results demonstrate the accuracy of this novel method.

scholarly journals INCREMENTAL REFINEMENT OF FAÇADE MODELS WITH ATTRIBUTE GRAMMAR FROM 3D POINT CLOUDS

2016 ◽  
Vol III-3 ◽  
pp. 311-316 ◽  
Author(s):  
Y. Dehbi ◽  
C. Staat ◽  
L. Mandtler ◽  
L. Pl¨umer
Keyword(s):  
A Priori ◽  
Point Clouds ◽  
3D Point Clouds ◽  
Architectural Patterns ◽  
Grammar Rules ◽  
Probability Densities ◽  
Parse Trees ◽  
Incremental Interpretation ◽  
Context Free ◽  
Building Reconstruction

Data acquisition using unmanned aerial vehicles (UAVs) has gotten more and more attention over the last years. Especially in the field of building reconstruction the incremental interpretation of such data is a demanding task. In this context formal grammars play an important role for the top-down identification and reconstruction of building objects. Up to now, the available approaches expect offline data in order to parse an a-priori known grammar. For mapping on demand an on the fly reconstruction based on UAV data is required. An incremental interpretation of the data stream is inevitable. This paper presents an incremental parser of grammar rules for an automatic 3D building reconstruction. The parser enables a model refinement based on new observations with respect to a weighted attribute context-free grammar (WACFG). The falsification or rejection of hypotheses is supported as well. The parser can deal with and adapt available parse trees acquired from previous interpretations or predictions. Parse trees derived so far are updated in an iterative way using transformation rules. A diagnostic step searches for mismatches between current and new nodes. Prior knowledge on fac¸ades is incorporated. It is given by probability densities as well as architectural patterns. Since we cannot always assume normal distributions, the derivation of location and shape parameters of building objects is based on a kernel density estimation (KDE). While the level of detail is continuously improved, the geometrical, semantic and topological consistency is ensured.

Optimal Paths for Polygonal Robots in SE(2)

2017 ◽  
Author(s):  
Monroe Kennedy ◽  
Dinesh Thakur ◽  
Vijay Kumar ◽  
M. Ani Hsieh ◽  
Subhrajit Bhattacharya
Keyword(s):  
Free Space ◽  
Optimal Path ◽  
Point Clouds ◽  
Linear Constraints ◽  
Minkowski Sum ◽  
Efficient Computation ◽  
Optimal Paths ◽  
Holonomic Robot ◽  
Novel Method ◽  
Basic Ideas

We consider navigation for a polygonal, holonomic robot in an obstacle filled environment in SE(2). We denote the configuration space of the robot as C. In order to determine the free space, obstacles are represented as point clouds then transformed into C. The point-wise Minkowski sum of the robot and obstacle points is then calculated in C by adding the vertices and points on the convex hull of robot to obstacle points for different robot configurations. We then find a seed path using either a graph search or sample based planner. This seed path is then used in our novel method to determine overlapping convex regions for each consecutive chord of the seed path. Our proposed method represents the collision free, traversable region by defining overlapping convex corridors defined by a set of linear constraints. Within these corridors we find feasible trajectories that optimize a specified cost functional. The generated corridors along with the initial and desired poses are then used to determine an optimal path that satisfies the specified objective within the same homotopy group as the seed path. The key contributions is the proposed methods’ ability to easily generate a set of convex, overlapping polytopes that effectively represent the traversable free space. This in turn lends itself to (a) efficient computation of optimal paths, and (b) extending these basic ideas to non-Euclidean spaces such as SE(2). We provide simulated examples and implement this algorithm on the KUKA youBot omni-directional base.

scholarly journals RegARD: Symmetry-Based Coarse Registration of Smartphone’s Colorful Point Clouds with CAD Drawings for Low-Cost Digital Twin Buildings

2021 ◽  
Vol 13 (10) ◽  
pp. 1882
Author(s):  
Yijie Wu ◽  
Jianga Shang ◽  
Fan Xue
Keyword(s):  
Degrees Of Freedom ◽  
Low Cost ◽  
Point Clouds ◽  
Data Sets ◽  
Digital Twin ◽  
3D Point Clouds ◽  
Coarse Registration ◽  
Smartphone Sensors ◽  
Novel Method ◽  
Twin Buildings

Coarse registration of 3D point clouds plays an indispensable role for parametric, semantically rich, and realistic digital twin buildings (DTBs) in the practice of GIScience, manufacturing, robotics, architecture, engineering, and construction. However, the existing methods have prominently been challenged by (i) the high cost of data collection for numerous existing buildings and (ii) the computational complexity from self-similar layout patterns. This paper studies the registration of two low-cost data sets, i.e., colorful 3D point clouds captured by smartphones and 2D CAD drawings, for resolving the first challenge. We propose a novel method named `Registration based on Architectural Reflection Detection’ (RegARD) for transforming the self-symmetries in the second challenge from a barrier of coarse registration to a facilitator. First, RegARD detects the innate architectural reflection symmetries to constrain the rotations and reduce degrees of freedom. Then, a nonlinear optimization formulation together with advanced optimization algorithms can overcome the second challenge. As a result, high-quality coarse registration and subsequent low-cost DTBs can be created with semantic components and realistic appearances. Experiments showed that the proposed method outperformed existing methods considerably in both effectiveness and efficiency, i.e., 49.88% less error and 73.13% less time, on average. The RegARD presented in this paper first contributes to coarse registration theories and exploitation of symmetries and textures in 3D point clouds and 2D CAD drawings. For practitioners in the industries, RegARD offers a new automatic solution to utilize ubiquitous smartphone sensors for massive low-cost DTBs.

scholarly journals AN EMPRICAL POINT ERROR MODEL FOR TLS DERIVED POINT CLOUDS

2016 ◽  
Vol XLI-B5 ◽  
pp. 557-563 ◽  
Author(s):  
Mustafa Ozendi ◽  
Devrim Akca ◽  
Hüseyin Topan
Keyword(s):  
Real World ◽  
Incidence Angle ◽  
A Priori ◽  
Laser Scanner ◽  
Point Clouds ◽  
Error Model ◽  
Random Errors ◽  
Data Set ◽  
3D Point Clouds

The random error pattern of point clouds has significant effect on the quality of final 3D model. The magnitude and distribution of random errors should be modelled numerically. This work aims at developing such an anisotropic point error model, specifically for the terrestrial laser scanner (TLS) acquired 3D point clouds. A priori precisions of basic TLS observations, which are the range, horizontal angle and vertical angle, are determined by predefined and practical measurement configurations, performed at real-world test environments. A priori precision of horizontal (𝜎<sub>𝜃</sub>) and vertical (𝜎<sub>𝛼</sub>) angles are constant for each point of a data set, and can directly be determined through the repetitive scanning of the same environment. In our practical tests, precisions of the horizontal and vertical angles were found as 𝜎<sub>𝜃</sub>=±36.6<sup>𝑐𝑐</sup> and 𝜎<sub>𝛼</sub>=±17.8<sup>𝑐𝑐</sup>, respectively. On the other hand, a priori precision of the range observation (𝜎<sub>𝜌</sub>) is assumed to be a function of range, incidence angle of the incoming laser ray, and reflectivity of object surface. Hence, it is a variable, and computed for each point individually by employing an empirically developed formula varying as 𝜎<sub>𝜌</sub>=±2−12 𝑚𝑚 for a FARO Focus X330 laser scanner. This procedure was followed by the computation of error ellipsoids of each point using the law of variance-covariance propagation. The direction and size of the error ellipsoids were computed by the principal components transformation. The usability and feasibility of the model was investigated in real world scenarios. These investigations validated the suitability and practicality of the proposed method.

scholarly journals INCREMENTAL REFINEMENT OF FAÇADE MODELS WITH ATTRIBUTE GRAMMAR FROM 3D POINT CLOUDS

2016 ◽  
Vol III-3 ◽  
pp. 311-316
Author(s):  
Y. Dehbi ◽  
C. Staat ◽  
L. Mandtler ◽  
L. Pl¨umer
Keyword(s):  
A Priori ◽  
Point Clouds ◽  
3D Point Clouds ◽  
Architectural Patterns ◽  
Grammar Rules ◽  
Probability Densities ◽  
Parse Trees ◽  
Incremental Interpretation ◽  
Context Free ◽  
Building Reconstruction

Data acquisition using unmanned aerial vehicles (UAVs) has gotten more and more attention over the last years. Especially in the field of building reconstruction the incremental interpretation of such data is a demanding task. In this context formal grammars play an important role for the top-down identification and reconstruction of building objects. Up to now, the available approaches expect offline data in order to parse an a-priori known grammar. For mapping on demand an on the fly reconstruction based on UAV data is required. An incremental interpretation of the data stream is inevitable. This paper presents an incremental parser of grammar rules for an automatic 3D building reconstruction. The parser enables a model refinement based on new observations with respect to a weighted attribute context-free grammar (WACFG). The falsification or rejection of hypotheses is supported as well. The parser can deal with and adapt available parse trees acquired from previous interpretations or predictions. Parse trees derived so far are updated in an iterative way using transformation rules. A diagnostic step searches for mismatches between current and new nodes. Prior knowledge on fac¸ades is incorporated. It is given by probability densities as well as architectural patterns. Since we cannot always assume normal distributions, the derivation of location and shape parameters of building objects is based on a kernel density estimation (KDE). While the level of detail is continuously improved, the geometrical, semantic and topological consistency is ensured.

scholarly journals USING MOBILE LASER SCANNING DATA FOR FEATURES EXTRACTION OF HIGH ACCURACY DRIVING MAPS

2016 ◽  
Vol XLI-B3 ◽  
pp. 433-439 ◽  
Author(s):  
Bisheng Yang ◽  
Yuan Liu ◽  
Fuxun Liang ◽  
Zhen Dong
Keyword(s):  
Laser Scanning ◽  
Human Error ◽  
Traffic Accidents ◽  
Three Dimensional ◽  
Point Clouds ◽  
High Accuracy ◽  
3D Point Clouds ◽  
Assistant Systems ◽  
Novel Method ◽  
High Flexibility

High Accuracy Driving Maps (HADMs) are the core component of Intelligent Drive Assistant Systems (IDAS), which can effectively reduce the traffic accidents due to human error and provide more comfortable driving experiences. Vehicle-based mobile laser scanning (MLS) systems provide an efficient solution to rapidly capture three-dimensional (3D) point clouds of road environments with high flexibility and precision. This paper proposes a novel method to extract road features (e.g., road surfaces, road boundaries, road markings, buildings, guardrails, street lamps, traffic signs, roadside-trees, power lines, vehicles and so on) for HADMs in highway environment. Quantitative evaluations show that the proposed algorithm attains an average precision and recall in terms of 90.6% and 91.2% in extracting road features. Results demonstrate the efficiencies and feasibilities of the proposed method for extraction of road features for HADMs.

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