There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

CPS data streams analytics based on machine learning for Cloud and Fog computing: a survey

The advances in Information Technologies have led to more complex road safety applications. These systems provide multiple possibilities for improving road transport. The integrated system that this paper presents deals with two aspects that have been identified as key topics: safety and efficiency. To this end, the development and implementation of an integrated advanced driver assistance system (ADAS) for rural and intercity environments is proposed. The system focuses mainly on single-carriageways roads, given the complexity of these environments compared to motorways and the high number of severe and fatal accidents on them. The proposed system is based on advanced perception techniques, vehicle automation and communications between vehicles (V2V) and with the infrastructure (V2I). Sensor fusion architecture based on computer vision and laser scanner technologies are developed. It allows real time detection and classification of obstacles, and the identification of potential risks. The driver receives this information and some warnings generated by the system. In case, he does not react in a proper way, the vehicle could perform autonomous actions (both on speed control or steering maneuvers) to improve safety and/or efficiency. Furthermore, a multimodal V2V and V2I communication system, based on GeoNetworking, facilitates the flow of information between vehicles and assists in the detection and information broadcasting processes. All this, combined with vehicle positioning, detailed digital maps and advanced map-matching algorithms, establish the decision algorithms of different ADAS systems. The applications developed include: adaptive cruise control with consumption optimization, overtaking assistance system in single-carriageways roads that takes into account appropriate speed evolution and identifies most suitable road stretches for the maneuver; assistance system in intersections with speed control during approximation maneuvers, and collision avoidance system with the possibility of evasive maneuvers. To this end, mathematical vehicle dynamics models have been used to ensure the stability, and propulsion system models are used to establish efficient patterns, Artificial Intelligence and simulation are used for experimentation and evaluation of algorithms to be implemented in the control unit. Finally, the system is designed to warn the driver if a risk is detected and, if necessary, to take control of the vehicle. The system has been implemented on a passenger car and has been tested in specific scenarios on a test track with satisfactory results. Language: en

https://cyberleninka.org/article/n/675714.pdf

Advanced Driver Assistance System for Road Environments to Improve Safety and Efficiency

Sensor setups consisting of a combination of 3D range scanner lasers and stereo vision systems are becoming a popular choice for on-board perception systems in vehicles; however, the combined use of both sources of information implies a tedious calibration process. We present a method for extrinsic calibration of lidar-stereo camera pairs without user intervention. Our calibration approach is aimed to cope with the constraints commonly found in automotive setups, such as low-resolution and specific sensor poses. To demonstrate the performance of our method, we also introduce a novel approach for the quantitative assessment of the calibration results, based on a simulation environment. Tests using real devices have been conducted as well, proving the usability of the system and the improvement over the existing approaches. Code is available at http://wiki.ros.org/velo2cam_calibration.

Automatic extrinsic calibration for lidar-stereo vehicle sensor setups

IoT-enabled Smart Factory Visibility and Traceability Using Laser-scanners

Driving Assistance Systems and Autonomous Driving applications require trustable detections. These demanding requirements need sensor fusion to provide information reliable enough. But data fusion presents the problem of data alignment in both rotation and translation. Laser scanner and video cameras are widely used in sensor fusion. Laser provides operation in darkness, long range detection and accurate measurement but lacks the means for reliable classification due to the limited information provided. The camera provides classification thanks to the amount of data provided but lacks accuracy for measurements and is sensitive to illumination conditions. Data alignment processes require supervised and accurate measurements, that should be performed by experts, or require specific patterns or shapes. This paper presents an algorithm for inter-calibration between the two sensors of our system, requiring only a flat surface for pitch and roll calibration and an obstacle visible for both sensors for determining the yaw. The advantage of this system is that it does not need any particular shape to be located in front of the vehicle apart from a flat surface, which is usually the road. This way, calibration can be achieved at virtually any time without human intervention.

Automatic laser and camera extrinsic calibration for data fusion using road plane

Reliability and accuracy are key in state of the art Driving Assistance Systems and Autonomous Driving applications. These applications make use of sensor fusion for trustable obstacle detection and classification in any meteorological and illumination condition. Laser scanner and camera are widely used as sensors to fuse because of its complementary capabilities. This paper presents some novel techniques for automatic and unattended data alignment between sensors, and Artificial Intelligence techniques are used to use laser point clouds not only for obstacle detection but also for classification.. Information fusion with classification information from both laser scanner and camera improves overall system reliability.

Laser Scanner and Camera Fusion for Automatic Obstacle Classification in ADAS Application

Given the growth of internet-based trading on a global level, there are several expected logistic challenges regarding the optimal transportation of large volumes of merchandise. With this in mind, the application of technologies such as computer vision and industrial robotics in facing these challenges presents significant advantages regarding the speed and reliability with which palletization tasks, a critical point in the merchandise transportation chain, can be performed. This paper presents a computer vision strategy for the localization and recognition of boxes in the context of a palletization process carried out by a robotic manipulator. The system operates using a Kinect 2.0 depth camera to capture a scene and processing the resulting point cloud. Obtained results permit the simultaneous recognition of up to 15 boxes, their position in space and their size characteristics within the workspace of the robot, with an average error of approximately 3 cm.

3D Object Pose Estimation for Robotic Packing Applications

Along with the technological advance of the industrial sector, the boom in process control systems has increased in recent years; however, the models employed in the industry are complex and non-linear, and conventional methods of modeling, identification and implementation of controllers are made from linear models in specific regions of operation. This linearization restricts the results and efficient data collection of the system, because it rejects useful information to optimize the processes. An inverted rotary pendulum is one of the most studied platforms in system identification and modeling theory, due to its known instability and complexity of its analytical model. This article shows the modeling and nonlinear identification of an inverted rotary pendulum of the manufacturer Quanser, along with its simulation, as part of the process of development of nonlinear controllers on complex plants, being the identified model the starting point for the design of these controllers, as a result of the process, an identified system is obtained for the simulated and real model with a low mean square error in its output signals process corresponding to pole and arm acceleration.

Identification of a non-linear model type inverted rotary pendulum

The increasingly complex tasks require an enormous effort in path planning within dynamic environments. This paper presents a efficient method for detecting collisions between a robot and its environment in order to prevent dangerous maneuvers. Our methods is based upon the transformation of each robot link and the environment in a set of bounding boxes. The aim of this kind of prismatic approximation is to detect a collision between objects in the workspace by testing collision between boxes from different objects. The computational cost of this approach has been tested in simulations, thus we have set up our environment with a HP20D robot and an obstacle, both represented by their corresponding chain of bounding boxes. The experiment implies to move the robot from an initial position, on the right of the obstacle, to a final position, on the left side of the obstacle, along a straight-line trajectory. The probe enabled us to check the correct behavior of a collision detector in a real situation.

C. H. Rodríguez-Garavito

Papers

Automatic laser and camera extrinsic calibration for data fusion using road plane

Laser Scanner and Camera Fusion for Automatic Obstacle Classification in ADAS Application

3D Object Pose Estimation for Robotic Packing Applications

Identification of a non-linear model type inverted rotary pendulum

Collision Detector for Industrial Robot Manipulators