Semi-Automated 3D Registration for Heterogeneous Unmanned Robots Based on Scale Invariant Method

This paper addresses the problem of 3D registration of outdoor environments combining heterogeneous datasets acquired from unmanned aerial (UAV) and ground (UGV) vehicles. In order to solve this problem, we introduced a novel Scale Invariant Registration Method (SIRM) for semi-automated registration of 3D point clouds. The method is capable of coping with an arbitrary scale difference between the point clouds, without any information about their initial position and orientation. Furthermore, the SIRM does not require having a good initial overlap between two heterogeneous datasets. Our method strikes an elegant balance between the existing fully automated 3D registration systems (which often fail in the case of heterogeneous datasets and harsh outdoor environments) and fully manual registration approaches (which are labour-intensive). The experimental validation of the proposed 3D heterogeneous registration system was performed on large-scale datasets representing unstructured and harsh outdoor environments, demonstrating the potential and benefits of the proposed 3D registration system in real-world environments

3D registration and integrated segmentation framework for heterogeneous unmanned robotic systems

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

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

This paper proposes a very effective method for data handling and preparation of the input 3D scans acquired from laser scanner mounted on the Unmanned Ground Vehicle (UGV). The main objectives are to improve and speed up the process of outliers removal for large-scale outdoor environments. This process is necessary in order to filter out the noise and to downsample the input data which will spare computational and memory resources for further processing steps, such as 3D mapping of rough terrain and unstructured environments. It includes the Voxel-subsampling and Fast Cluster Statistical Outlier Removal (FCSOR) subprocesses. The introduced FCSOR represents an extension on the Statistical Outliers Removal (SOR) method which is effective for both homogeneous and heterogeneous point clouds. This method is evaluated on real data obtained in outdoor environment

Fast Iterative 3D Mapping for Large-Scale Outdoor Environments with Local Minima Escape Mechanism

This paper introduces a novel iterative 3D mapping framework for large scale natural terrain and complex environments. The framework is based on an Iterative-Closest-Point (ICP) algorithm and an iterative error minimization mechanism, allowing robust 3D map registration. This was accomplished by performing pairwise scan registrations without any prior known pose estimation information and taking into account the measurement uncertainties due to the 6D coordinates (translation and rotation) deviations in the acquired scans. Since the ICP algorithm does not guarantee to escape from local minima during the mapping, new algorithms for the local minima estimation and local minima escape process were proposed. The proposed framework is validated using large scale field test data sets. The experimental results were compared with those of standard, generalized and non-linear ICP registration methods and the performance evaluation is presented, showing improved performance of the proposed 3D mapping framework

Decreased thermal tolerance under recurrent heat stress conditions explains summer mass mortality of the blue mussel Mytilus edulis

Extreme events such as heat waves have increased in frequency and duration over the last decades. Under future climate scenarios, these discrete climatic events are expected to become even more recurrent and severe. Heat waves are particularly important on rocky intertidal shores, one of the most thermally variable and stressful habitats on the planet. Intertidal mussels, such as the blue mussel Mytilus edulis, are ecosystem engineers of global ecological and economic importance, that occasionally suffer mass mortalities. This study investigates the potential causes and consequences of a mass mortality event of M. edulis that occurred along the French coast of the eastern English Channel in summer 2018. We used an integrative, climatological and ecophysiological methodology based on three complementary approaches. We first showed that the observed mass mortality (representing 49 to 59% of the annual commercial value of local recreational and professional fisheries combined) occurred under relatively moderate heat wave conditions. This result indicates that M. edulis body temperature is controlled by non-climatic heat sources instead of climatic heat sources, as previously reported for intertidal gastropods. Using biomimetic loggers (i.e. 'robomussels'), we identified four periods of 5 to 6 consecutive days when M. edulis body temperatures consistently reached more than 30 °C, and occasionally more than 35 °C and even more than 40 °C. We subsequently reproduced these body temperature patterns in the laboratory to infer M. edulis thermal tolerance under conditions of repeated heat stress. We found that thermal tolerance consistently decreased with the number of successive daily exposures. These results are discussed in the context of an era of global change where heat events are expected to increase in intensity and frequency, especially in the eastern English Channel where the low frequency of commercially exploitable mussels already questions both their ecological and commercial sustainability.Funding Agency French Ministere de l'Enseignement Superieur et de la Recherche Region Hauts-de-France European Funds for Regional Economical Development Pierre Hubert Curien PESSOA Felloswhip Fundacao para a Ciencia e Tecnologia (FCT-MEC, Portugal) IF/01413/2014/CP1217/CT0004 National Research Foundation - South Africa 64801 South African Research Chairs Initiative (SARChI) of the Department of Science and Technology National Research Foundation - South Africainfo:eu-repo/semantics/publishedVersio

Dense 3D Structure and Motion Estimation as an Aid for Robot Navigation

Three-dimensional scene reconstruction is an important tool in many applications varying from computer graphics to mobile robot navigation. In this paper, we focus on the robotics application, where the goal is to estimate the 3D rigid motion of a mobile robot and to reconstruct a dense three-dimensional scene representation. The reconstruction problem can be subdivided into a number of subproblems. First, the egomotion has to be estimated. For this, the camera (or robot) motion parameters are iteratively estimated by reconstruction of the epipolar geometry. Secondly, a dense depth map is calculated by fusing sparse depth information from point features and dense motion information from the optical flow in a variational framework. This depth map corresponds to a point cloud in 3D space, which can then be converted into a model to extract information for the robot navigation algorithm. Here, we present an integrated approach for the structure and egomotion estimation problem

A Behaviour-Based Control and Software Architecture for the Visually Guided Gobudem Outdoor Mobile Robot

The design of outdoor autonomous robots requires the careful consideration and integration of multiple aspects: sensors and sensor data fusion, design of a control and software architecture, design of a path planning algorithm and robot control. This paper describes partial aspects of this research work, which is aimed at developing a semiautonomous outdoor robot for risky interventions. This paper focuses on three main aspects of the design process: visual sensing using stereo vision and image motion analysis, design of a behaviourbased control architecture and implementation of modular software architecture

6D SLAM wykorzystujacy obliczenia GPGPU

Abstract: The main goal was to improve a state of the art 6D SLAM algorithm with a new GPGPU-based implementation of data registration module. Data registration is based on ICP (Iterative Closest Point) algorithm that is fully implemented in the GPU with NVIDIA FERMI architecture. In our research we focus on mobile robot inspection intervention systems applicable in hazardous environments. The goal is to deliver a complete system capable of being used in real life. In this paper we demonstrate our achievements in the field of on line robot localization and mapping. We demonstrated an experiment in real large environment. We compared two strategies of data alingment - simple ICP and ICP using so called meta scan.Głównym celem jest artykułu jest usprawnienie algorytmu 6D SLAM za pomocą implementacji modułu rejestracji danych wykorzystującą obliczenia równoległe. Moduł rejestracji danych jest oparty o algorytm ICP (ang. Iterative Closest Point), który został w pełni zaimplementowany w architekturze GPU NVIDIA FERMI. W naszych badaniach koncentrujemy się na mobilnych systemach robotycznych inspekcyjno-interwencyjnych dedykowanych do pracy w niebezpiecznym środowisku. Celem jest opracowanie kompletnego systemu, który może być wykorzystany w realnej aplikacji. W tym artykule przedstawiamy nasze rezultaty w zakresie lokalizacji i budowy mapy w trybie on-line. Przedstawiamy eksperyment w rzeczywistym, rozległym środowisku. Zostały porównane dwie strategie dopasowywania danych, klasyczna oraz wykorzystująca tzw. meta scan