scholarly journals Global localization of 3D point clouds in building outline maps of urban outdoor environments

2017 ◽  
Vol 1 (4) ◽  
pp. 429-441 ◽  
Author(s):  
Christian Landsiedel ◽  
Dirk Wollherr
Keyword(s):  
Point Clouds ◽  
Global Localization ◽  
3D Point Clouds ◽  
Outdoor Environments

Sparse semantic map building and relocalization for UGV using 3D point clouds in outdoor environments

2020 ◽  
Vol 400 ◽  
pp. 333-342
Author(s):  
Fei Yan ◽  
Jiawei Wang ◽  
Guojian He ◽  
Huan Chang ◽  
Yan Zhuang
Keyword(s):  
Point Clouds ◽  
Map Building ◽  
Semantic Map ◽  
3D Point Clouds ◽  
Outdoor Environments

scholarly journals SegMap: Segment-based mapping and localization using data-driven descriptors

2019 ◽  
Vol 39 (2-3) ◽  
pp. 339-355 ◽  
Author(s):  
Renaud Dubé ◽  
Andrei Cramariuc ◽  
Daniel Dugas ◽  
Hannes Sommer ◽  
Marcin Dymczyk ◽  
...  
Keyword(s):  
Large Scale ◽  
Structural Changes ◽  
Mobile Robotics ◽  
State Of The Art ◽  
Point Clouds ◽  
Open Loop ◽  
Data Driven ◽  
Global Localization ◽  
3D Point Clouds ◽  
Coarse Information

Precisely estimating a robot’s pose in a prior, global map is a fundamental capability for mobile robotics, e.g., autonomous driving or exploration in disaster zones. This task, however, remains challenging in unstructured, dynamic environments, where local features are not discriminative enough and global scene descriptors only provide coarse information. We therefore present SegMap: a map representation solution for localization and mapping based on the extraction of segments in 3D point clouds. Working at the level of segments offers increased invariance to view-point and local structural changes, and facilitates real-time processing of large-scale 3D data. SegMap exploits a single compact data-driven descriptor for performing multiple tasks: global localization, 3D dense map reconstruction, and semantic information extraction. The performance of SegMap is evaluated in multiple urban driving and search and rescue experiments. We show that the learned SegMap descriptor has superior segment retrieval capabilities, compared with state-of-the-art handcrafted descriptors. As a consequence, we achieve a higher localization accuracy and a 6% increase in recall over state-of-the-art handcrafted descriptors. These segment-based localizations allow us to reduce the open-loop odometry drift by up to 50%. SegMap is open-source available along with easy to run demonstrations.

scholarly journals LocNet: Global Localization in 3D Point Clouds for Mobile Vehicles

2018 ◽  
Author(s):  
Huan Yin ◽  
Li Tang ◽  
Xiaqing Ding ◽  
Yue Wang ◽  
Rong Xiong
Keyword(s):  
Point Clouds ◽  
Global Localization ◽  
3D Point Clouds

scholarly journals 3D Registration and Integrated Segmentation Framework for Heterogeneous Unmanned Robotic Systems

2020 ◽  
Vol 12 (10) ◽  
pp. 1608 ◽  
Author(s):  
Haris Balta ◽  
Jasmin Velagic ◽  
Halil Beglerovic ◽  
Geert De Cubber ◽  
Bruno Siciliano
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Point Clouds ◽  
Local Minima ◽  
3D Registration ◽  
Registration System ◽  
Environmental Models ◽  
3D Point Clouds ◽  
Outdoor Environments ◽  
Segmentation Framework

The paper proposes a novel framework for registering and segmenting 3D point clouds of large-scale natural terrain and complex environments coming from a multisensor heterogeneous robotics system, consisting of unmanned aerial and ground vehicles. This framework involves data acquisition and pre-processing, 3D heterogeneous registration and integrated multi-sensor based segmentation modules. The first module provides robust and accurate homogeneous registrations of 3D environmental models based on sensors’ measurements acquired from the ground (UGV) and aerial (UAV) robots. For 3D UGV registration, we proposed a novel local minima escape ICP (LME-ICP) method, which is based on the well known iterative closest point (ICP) algorithm extending it by the introduction of our local minima estimation and local minima escape mechanisms. It did not require any prior known pose estimation information acquired from sensing systems like odometry, global positioning system (GPS), or inertial measurement units (IMU). The 3D UAV registration has been performed using the Structure from Motion (SfM) approach. In order to improve and speed up the process of outliers removal for large-scale outdoor environments, we introduced the Fast Cluster Statistical Outlier Removal (FCSOR) method. This method was used to filter out the noise and to downsample the input data, which will spare computational and memory resources for further processing steps. Then, we co-registered a point cloud acquired from a laser ranger (UGV) and a point cloud generated from images (UAV) generated by the SfM method. The 3D heterogeneous module consists of a semi-automated 3D scan registration system, developed with the aim to overcome the shortcomings of the existing fully automated 3D registration approaches. This semi-automated registration system is based on the novel Scale Invariant Registration Method (SIRM). The SIRM provides the initial scaling between two heterogenous point clouds and provides an adaptive mechanism for tuning the mean scale, based on the difference between two consecutive estimated point clouds’ alignment error values. Once aligned, the resulting homogeneous ground-aerial point cloud is further processed by a segmentation module. For this purpose, we have proposed a system for integrated multi-sensor based segmentation of 3D point clouds. This system followed a two steps sequence: ground-object segmentation and color-based region-growing segmentation. The experimental validation of the proposed 3D heterogeneous registration and integrated segmentation framework was performed on large-scale datasets representing unstructured outdoor environments, demonstrating the potential and benefits of the proposed semi-automated 3D registration system in real-world environments.

scholarly journals Thermal 3D mapping for object detection in dynamic scenes

2014 ◽  
Vol II-1 ◽  
pp. 53-60 ◽  
Author(s):  
M. Weinmann ◽  
J. Leitloff ◽  
L. Hoegner ◽  
B. Jutzi ◽  
U. Stilla ◽  
...  
Keyword(s):  
Object Detection ◽  
Point Clouds ◽  
3D Mapping ◽  
Geometric Calibration ◽  
Cloud Data ◽  
3D Point Clouds ◽  
Feature Correspondences ◽  
Outdoor Environments ◽  
Intensity Images ◽  
3D Scene

The automatic analysis of 3D point clouds has become a crucial task in photogrammetry, remote sensing and computer vision. Whereas modern range cameras simultaneously provide both range and intensity images with high frame rates, other devices can be used to obtain further information which could be quite valuable for tasks such as object detection or scene interpretation. In particular thermal information offers many advantages, since people can easily be detected as heat sources in typical indoor or outdoor environments and, furthermore, a variety of concealed objects such as heating pipes as well as structural properties such as defects in isolation may be observed. In this paper, we focus on thermal 3D mapping which allows to observe the evolution of a dynamic 3D scene over time. We present a fully automatic methodology consisting of four successive steps: (i) a radiometric correction, (ii) a geometric calibration, (iii) a robust approach for detecting reliable feature correspondences and (iv) a co-registration of 3D point cloud data and thermal information via a RANSAC-based EPnP scheme. For an indoor scene, we demonstrate that our methodology outperforms other recent approaches in terms of both accuracy and applicability. We additionally show that efficient straightforward techniques allow a categorization according to background, people, passive scene manipulation and active scene manipulation.

3D traversability awareness for rough terrain mobile robots

Sensor Review ◽  
2014 ◽  
Vol 34 (2) ◽  
pp. 220-232 ◽  
Author(s):  
Giulio Reina ◽  
Mauro Bellone ◽  
Luigi Spedicato ◽  
Nicola Ivan Giannoccaro
Keyword(s):  
Mobile Robots ◽  
Field Experiments ◽  
Point Clouds ◽  
Unmanned Vehicles ◽  
Outdoor Environment ◽  
Local Descriptor ◽  
Content Type ◽  
3D Point Clouds ◽  
Outdoor Environments ◽  
Indoor And Outdoor

Purpose – This research aims to address the issue of safe navigation for autonomous vehicles in highly challenging outdoor environments. Indeed, robust navigation of autonomous mobile robots over long distances requires advanced perception means for terrain traversability assessment. Design/methodology/approach – The use of visual systems may represent an efficient solution. This paper discusses recent findings in terrain traversability analysis from RGB-D images. In this context, the concept of point as described only by its Cartesian coordinates is reinterpreted in terms of local description. As a result, a novel descriptor for inferring the traversability of a terrain through its 3D representation, referred to as the unevenness point descriptor (UPD), is conceived. This descriptor features robustness and simplicity. Findings – The UPD-based algorithm shows robust terrain perception capabilities in both indoor and outdoor environment. The algorithm is able to detect obstacles and terrain irregularities. The system performance is validated in field experiments in both indoor and outdoor environments. Research limitations/implications – The UPD enhances the interpretation of 3D scene to improve the ambient awareness of unmanned vehicles. The larger implications of this method reside in its applicability for path planning purposes. Originality/value – This paper describes a visual algorithm for traversability assessment based on normal vectors analysis. The algorithm is simple and efficient providing fast real-time implementation, since the UPD does not require any data processing or previously generated digital elevation map to classify the scene. Moreover, it defines a local descriptor, which can be of general value for segmentation purposes of 3D point clouds and allows the underlining geometric pattern associated with each single 3D point to be fully captured and difficult scenarios to be correctly handled.

scholarly journals Fast Statistical Outlier Removal Based Method for Large 3D Point Clouds of Outdoor Environments

2018 ◽  
Vol 51 (22) ◽  
pp. 348-353 ◽  
Author(s):  
Haris Balta ◽  
Jasmin Velagic ◽  
Walter Bosschaerts ◽  
Geert De Cubber ◽  
Bruno Siciliano
Keyword(s):  
Point Clouds ◽  
Outlier Removal ◽  
3D Point Clouds ◽  
Outdoor Environments

scholarly journals Symmetry Analysis of Oriental Polygonal Pagodas Using 3D Point Clouds for Cultural Heritage

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1228
Author(s):  
Ting On Chan ◽  
Linyuan Xia ◽  
Yimin Chen ◽  
Wei Lang ◽  
Tingting Chen ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Geometric Model ◽  
Digital Camera ◽  
Point Clouds ◽  
Symmetry Analysis ◽  
3D Point Clouds ◽  
Transmission Towers ◽  
Aerial Vehicle

Ancient pagodas are usually parts of hot tourist spots in many oriental countries due to their unique historical backgrounds. They are usually polygonal structures comprised by multiple floors, which are separated by eaves. In this paper, we propose a new method to investigate both the rotational and reflectional symmetry of such polygonal pagodas through developing novel geometric models to fit to the 3D point clouds obtained from photogrammetric reconstruction. The geometric model consists of multiple polygonal pyramid/prism models but has a common central axis. The method was verified by four datasets collected by an unmanned aerial vehicle (UAV) and a hand-held digital camera. The results indicate that the models fit accurately to the pagodas’ point clouds. The symmetry was realized by rotating and reflecting the pagodas’ point clouds after a complete leveling of the point cloud was achieved using the estimated central axes. The results show that there are RMSEs of 5.04 cm and 5.20 cm deviated from the perfect (theoretical) rotational and reflectional symmetries, respectively. This concludes that the examined pagodas are highly symmetric, both rotationally and reflectionally. The concept presented in the paper not only work for polygonal pagodas, but it can also be readily transformed and implemented for other applications for other pagoda-like objects such as transmission towers.

scholarly journals Using Least-Squares Residuals to Assess the Stochasticity of Measurements—Example: Terrestrial Laser Scanner and Surface Modeling

2021 ◽  
Vol 5 (1) ◽  
pp. 59
Author(s):  
Gaël Kermarrec ◽  
Niklas Schild ◽  
Jan Hartmann
Keyword(s):  
Least Squares ◽  
Laser Scanner ◽  
Real Data ◽  
Point Clouds ◽  
Statistical Testing ◽  
Normal Vector ◽  
Engineering Structures ◽  
3D Point Clouds ◽  
Least Squares Adjustment ◽  
Correlation Parameters

Terrestrial laser scanners (TLS) capture a large number of 3D points rapidly, with high precision and spatial resolution. These scanners are used for applications as diverse as modeling architectural or engineering structures, but also high-resolution mapping of terrain. The noise of the observations cannot be assumed to be strictly corresponding to white noise: besides being heteroscedastic, correlations between observations are likely to appear due to the high scanning rate. Unfortunately, if the variance can sometimes be modeled based on physical or empirical considerations, the latter are more often neglected. Trustworthy knowledge is, however, mandatory to avoid the overestimation of the precision of the point cloud and, potentially, the non-detection of deformation between scans recorded at different epochs using statistical testing strategies. The TLS point clouds can be approximated with parametric surfaces, such as planes, using the Gauss–Helmert model, or the newly introduced T-splines surfaces. In both cases, the goal is to minimize the squared distance between the observations and the approximated surfaces in order to estimate parameters, such as normal vector or control points. In this contribution, we will show how the residuals of the surface approximation can be used to derive the correlation structure of the noise of the observations. We will estimate the correlation parameters using the Whittle maximum likelihood and use comparable simulations and real data to validate our methodology. Using the least-squares adjustment as a “filter of the geometry” paves the way for the determination of a correlation model for many sensors recording 3D point clouds.

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