Improvement of Control and Supervision of Web Connected Mobile Robots Using PPU Computation

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Autonomous Mobile Mapping Robots: Key Software Components

This chapter discusses key software components of autonomous mobile mapping robots equipped with an inertial measurement unit (IMU) and light detection and ranging (LiDAR). In recent years, new LiDARs with nonrepetitive scanning pattern have appeared in the market. They are also equipped with an IMU; thus, the front end of simultaneous localization and mapping (SLAM)—a robust LiDAR-inertial odometry framework—significantly improves unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAV) in 3D mapping scenarios. Our study incorporates FAST-LIO as the front end of SLAM. The main focus is a lightweight back-end implementation of pose graph simultaneous localization and mapping (SLAM). It is an alternative solution to state-of-the-art g2o or GTSAM implementations. We also elaborate on iterative closest point, normal distributions transform, and their extension for multiview 3D data registration/refinement. It is based on C++ using Eigen library. This chapter also discusses path planning in already mapped environment. All software components are available as open-source projects

ICARUS Training and Support System

The ICARUS unmanned tools act as gatherers, which acquire enormous amount of information. The management of all these data requires the careful consideration of an intelligent support system. This chapter discusses the High-Performance Computing (HPC) support tools, which were developed for rapid 3D data extraction, combination, fusion, segmentation, classification and rendering. These support tools were seamlessly connected to a training framework. Indeed, training is a key in the world of search and rescue. Search and rescue workers will never use tools on the field for which they have not been extensively trained beforehand. For this reason, a comprehensive serious gaming training framework was developed, supporting all ICARUS unmanned vehicles in realistic 3D-simulated (based on inputs from the support system) and real environments

6D SLAM with GPGPU computation

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 alignment -simple ICP and ICP using so called meta scan

Open source, open hardware hand-held mobile mapping system for large scale surveys

This paper presents open-source software for large-scale 3D mapping using an open-hardware hand-held measurement device. This work is dedicated to educational and research purposes. This software is composed of three components: LIDAR odometry, single-session refinement and multi-session refinement. Data refinement uses a pose-graph loop closure technique and an Iterative Closest Point algorithm to minimize the error of the edge. The results are 3D point clouds in LAZ data format (compressed LAS - LIDAR Aerial Survey). It was tested in many real-world scenarios/applications: city-level 3D mapping, culture heritage, creating ground truth data for mobile robots, precise forestry, and large-scale indoor 3D mapping. This software can run on Linux and Windows machines, it does not incorporate GPU computing. It is advised to use at least 32 GB of RAM to cope with large data sets. The optimization framework is implemented from scratch using the Eigen library, thus there is not need to install any additional libraries such as Ceres, g2o, GTSAM, manif, Sophus etc

Affordable Robotic Mobile Mapping System Based on Lidar with Additional Rotating Planar Reflector

This paper describes an affordable robotic mobile 3D mapping system. It is built with Livox Mid–40 lidar with a conic field of view extended by a custom rotating planar reflector. This 3D sensor is compared with the more expensive Velodyne VLP 16 lidar. It is shown that the proposed sensor reaches satisfactory accuracy and range. Furthermore, it is able to preserve the metric accuracy and non–repetitive scanning pattern of the unmodified sensor. Due to preserving the non–repetitive scan pattern, our system is capable of covering the entire field of view of 38.4 × 360 degrees, which is an added value of conducted research. We show the calibration method, mechanical design, and synchronization details that are necessary to replicate our system. This work extends the applicability of solid–state lidars since the field of view can be reshaped with minimal loss of measurement properties. The solution was part of a system that was evaluated during the 3rd European Robotics Hackathon in the Zwentendorf Nuclear Power Plant. The experimental part of the paper demonstrates that our affordable robotic mobile 3D mapping system is capable of providing 3D maps of a nuclear facility that are comparable to the more expensive solution

Calibration of Planar Reflectors Reshaping LiDAR’s Field of View

This paper describes the calibration method for calculating parameters (position and orientation) of planar reflectors reshaping LiDAR’s (light detection and ranging) field of view. The calibration method is based on the reflection equation used in the ICP (Iterative Closest Point) optimization. A novel calibration process as the multi-view data registration scheme is proposed; therefore, the poses of the measurement instrument and parameters of planar reflectors are calculated simultaneously. The final metric measurement is more accurate compared with parameters retrieved from the mechanical design. Therefore, it is evident that the calibration process is required for affordable solutions where the mechanical design can differ from the inaccurate assembly. It is shown that the accuracy is less than 20 cm for almost all measurements preserving long-range capabilities. The experiment is performed based on Livox Mid-40 LiDAR augmented with six planar reflectors. The ground-truth data were collected using Z + F IMAGER 5010 3D Terrestrial Laser Scanner. The calibration method is independent of mechanical design and does not require any fiducial markers on the mirrors. This work fulfils the gap between rotating and Solid-State LiDARs since the field of view can be reshaped by planar reflectors, and the proposed method can preserve the metric accuracy. Thus, such discussion concludes the findings. We prepared an open-source project and provided all the necessary data for reproducing the experiments. That includes: Complete open-source code, the mechanical design of reflector assembly and the dataset which was used in this paper

Affordable Robotic Mobile Mapping System Based on Lidar with Additional Rotating Planar Reflector

This paper describes an affordable robotic mobile 3D mapping system. It is built with Livox Mid–40 lidar with a conic field of view extended by a custom rotating planar reflector. This 3D sensor is compared with the more expensive Velodyne VLP 16 lidar. It is shown that the proposed sensor reaches satisfactory accuracy and range. Furthermore, it is able to preserve the metric accuracy and non–repetitive scanning pattern of the unmodified sensor. Due to preserving the non–repetitive scan pattern, our system is capable of covering the entire field of view of 38.4 × 360 degrees, which is an added value of conducted research. We show the calibration method, mechanical design, and synchronization details that are necessary to replicate our system. This work extends the applicability of solid–state lidars since the field of view can be reshaped with minimal loss of measurement properties. The solution was part of a system that was evaluated during the 3rd European Robotics Hackathon in the Zwentendorf Nuclear Power Plant. The experimental part of the paper demonstrates that our affordable robotic mobile 3D mapping system is capable of providing 3D maps of a nuclear facility that are comparable to the more expensive solution