Scanning Laser Rangefinder Research Articles

Разработка метода определения угла наклона опор контактной сети железнодорожного транспорта

This paper discusses a new method for measuring and calculating the angle of inclination of a railway support or a contact network support, using an unmanned aerial vehicle flying along a straight path, parallel to the railway track. A review of existing measurement methods is carried out, their advantages and disadvantages are indicated. In the method under consideration, it is proposed to measure angles and distances with six laser scanning rangefinders installed in threes on horizontal and inclined planes on an unmanned aerial vehicle. This allows you to increase the speed and accuracy of determining the angle of inclination of vertical supports. The calculations use the minimum distances from the laser scanning range finder to the top and bottom of the support surface. The formulas use geometric relationships and the cosine theorem to calculate the roll of supports taking into account their taper. Measuring distances and angles three times allows for averaging over them, which significantly increases the accuracy of calculations. A model experiment is carried out on a model of a reinforced concrete contact network support in four orientations. A comparison is made between theoretically calculated and experimentally measured distances and inclination angles. The accuracy of parameter determination complies with regulatory requirements.

Method of Classification the Mobile Robot Workspace Based on the Analysis of a 3D Point Cloud

One of the main and most difficult tasks in the development of automotive systems is the classification of the workspace of a mobile robot. Based on the classification results, a local map of the area is built, with the help of which, then, the robot trajectory is planned. The article describes a method of classification the working area of an autonomous mobile robot moving in rough terrain. The developed classification method is based on the analysis of a three-dimensional point cloud obtained by a laser scan- ning 3D rangefinder. Using a scanning laser rangefinder allows you to classify the robot  s motion zone at any time of the day or year. A set of classification features is proposed, the calculation of which is carried out using the least square method and elements of probability theory and mathematical statistics. The classification of the robot workspace is carried out by four classes: "Flat surface", "Small roughness", "Large roughness" and "Obstacle". Each class characterizes the degree of passability of the robot movement surface. Classification results are saved as a local map of passability. In each cell of such a map a number is written that characterizes the passability of the region of the working area bounded by this cell. The developed classifier is integrated into the on-board control system of the wheeled mobile robot. The results of experimental studies confirming the efficiency and effectiveness of the proposed classification method are presented. The accuracy of class recognition of the mobile robot workspace has been determined. The developed classifier successfully operates in various conditions, including in winter and at dusk, but at the same time it has limitations when working in conditions of natural noise, such as rain, snow. The average classification accuracy with the minimal influence of natural noise is 92.3 %, and the execution time of each iteration does not exceed 0.085 s, which allows using developed classifier as a part of on-board control systems of autonomous mobile robots.

Development of a low-cost driver assistance system for tractors in sugarcane fields using a scanning-laser range finder

Development of a low-cost driver assistance system for tractors in sugarcane fields using a scanning-laser range finder

Laser-scanning rangefinder fixed on a gimbal with two degrees of freedom

A laser-scanning rangefinder mounted on a gimbal with two degrees of freedom is presented. The rangefinder can be used as part of a navigation system of an unmanned aerial vehicle to avoid obstacles or prevent collisions. Compared to stereo cameras, the device requires significantly less computing resources and is less dependent on lighting conditions. Compared to integrated lidars, the cost of the device is by an order lower. А model of the device was developed and an obstacle avoidance flight was simulated in the Gazebo simulator. The PX4/Avoidance software was used as an autopilot. As a result of a model experiment, we found that a scanning laser rangefinder can provide autonomous navigation with obstacle avoidance.

Scan Matching for Navigation of a Mobile Robot in Semi-Structured Terrain Conditions

To ensure unmanned autonomous movement of ground robotic means, it is required to accurately determine the position and orientationof the robot. The present study is related to the estimation of coordinates by comparing the scans of a laser scanning rangefinder in conditionsof semi-structed infrastructure and the absence of a global satellite communications signal. The existing methods of comparing scans havesignificant drawbacks in the conditions of movement over a semi-structured terrain, associated both with the processing time of data fromthe laser scanning rangefinder, and with the quality of the results obtained. The scan is preliminarily placed in a map consisting of cells.Each cell of around point scan is described by forces represented by the laws of physics or probability theory. In the cells of the map, wetake into account the mutual influence of all forces from each point of the scan and thus we obtain the resulting artificial potential field ofthe scan. The position of the robot is estimated by the change in the number of acting forces of one scan per points of the next scan takinginto account their direction. We estimate the orientation of the robot based on the sum of the vector products of the forces and distancesto the given forces acting on the points of the next scan. This method allows you to calculate the displacement of the robot between scansregardless of road conditions and terrain. This article presents the results of an experimental verification of the method on a mock-up of amobile robot equipped with a Velodyne HDL-32 LIDAR. We indicate the operating conditions of the method for a given LIDAR, as wellas the time spent on calculating the bias estimate. Given the peculiarities of the LIDAR, we present a method for eliminating the DopplerEffect (distortion) for the original point cloud. A comparative analysis of the developed method for integrating wheel odometry data, inertialand satellite navigation using the Extended Kalman Filter shows the applicability of this method to assess the position and orientation of therobot in conditions of its movement over rough terrain.

Efficient Non-Odometry Method for Environment Mapping and Localisation of Mobile Robots

Abstract The paper presents the simple algorithm of simultaneous localisation and mapping (SLAM) without odometry information. The proposed algorithm is based only on scanning laser range finder. The theoretical foundations of the proposed method are presented. The most important element of the work is the experimental research. The research underlying the paper encompasses several tests, which were carried out to build the environment map to be navigated by the mobile robot in conjunction with the trajectory planning algorithm and obstacle avoidance.

About One Way to Increase the Accuracy of Navigation System for Ground Wheeled Robot Used in Aircraft Parking

ABSTRACT The article discusses a ground-based wheeled robot (GWR), designed to operate at airfields, in particular at aircraft parking lots for the purpose of patrolling or photographing the lower part of the aircraft. The purpose of the article is to find a way to increase the accuracy of the navigation system for a ground wheeled robot used in aircraft parking. All initial and final calculations were performed in the navigation coordinate system (CS). Also, the starting CS (SCS) and CS associated with the GWR were used. A trajectory of detouring the aircraft landing gear racks was obtained, at which the influence of aircraft position errors at the airfield parking and scanning laser range finder (LIDAR) measurement errors on the accuracy of determining the location coordinates of the aircraft and GWR can be minimized. The influence of linear and angular errors of the aircraft position on the value of the error in determining the location of the GWR was studied. A correction structure was developed based on Kalman filter of GWR navigation system according to the calculated coordinates. The results of modeling the operation of integrated navigation system with the proposed correction channel were demonstrated, which have shown the effectiveness of the proposed correction method. Based on the analysis of the indicated GWR navigation systems, it can be concluded that the features of the use of additional sensors are determined by the purpose of GWR and, accordingly, the trajectory of its movement. The proposed correction algorithm for the navigation system based on LIDAR measurements based on the initial research is an adequate and effective alternative to using GPS in limited conditions.

Increasing measurement accuracy of a chickpea pile weight estimation tool using Moore-neighbor tracing algorithm in sphericity calculation

In this study, a new perspective for increasing the measurement accuracy of a chickpea pile volume and weight estimation tool is proposed. The system proposed uses the principles of machine learning methods and focuses on the adaptation of Moore-neighbor tracing algorithm in sphericity calculation of chickpeas. An experimental setup including a two degrees of freedom moving mechanism, a low-cost laser scanning rangefinder sensor, and a vision unit combining a camera and image processing algorithm is constructed. The methodology proposed is tested to estimate the weight of a chickpea pile using this experimental setup. In order to make comparisons, the estimations are also performed by the use of approaches proposed in the literature. The results of the experimental studies show that at least 5% weight estimation error is obtained when the estimation procedures given in the literature are used. On the other hand, the algorithm proposed in this study yields estimation error less than 0.57%. The details of the procedure proposed, the experimental setup designed and built, the computational environment developed and the experiments conducted are presented in this study.

Advancing Sustainability in Tree Crop Pest Management: Refining Spray Application Rate with a Laser-guided Variable-rate Sprayer in Apple Orchards

Advanced variable-rate spray technology, which applies pesticides based on real-time scanning laser rangefinder measurements of plant presence, size, and density, was developed and retrofitted to existing sprayers. Experiments were conducted to characterize the application of four programmed spray rates (0.03, 0.05, 0.07, or 0.09 L·m−3 of crop geometric volume) when applied to Malus domestica Borkh. ‘Golden Delicious’ apple trees using this crop sensing technology. Water-sensitive cards (WSCs) were used as samplers to quantify spray coverage, deposits, and deposit density in the target and nontarget areas, and an overspray index based on a threshold of greater than 30% coverage was calculated. The application rate ranged from 262 L·ha−1 at the programmed spray rate of 0.03 L·m−3 to 638 L·ha−1 at the rate of 0.09 L·m−3. For a given WSC position, spray coverage and deposits increased as the spray rate increased. WSC positions 1 and 2 were oversprayed at all rates. The effect of spray rate on deposit density varied with WSC positions, with high densities achieved by low spray rates for WSCs closest to the sprayer but by high spray rates for WSCs positioned either deeper within or under the canopy. When coalescing deposits were accounted for, deposit densities met or exceeded the recommended pesticide application thresholds (insecticides 20–30 droplets/cm2; fungicides 50–70 droplets/cm2) at all WSC positions for each spray rate tested. The lowest spray rate reduced off-target loss to the orchard floor by 81% compared with the highest rate, dramatically reducing potential exposure to nontarget organisms, such as foraging pollinators, to come into contact with pesticide residues. Applying the lowest rate of 0.03 L·m−3 met deposit density efficacy levels while reducing spray volume by 83% compared with the orchard standard application of 1540 L·ha−1 and by 87% compared with the 1950 L·ha−1 application rate recommended when using the tree row volume method. Thus, there is potential for growers to refine pesticide application rates to further achieve significant pesticide cost savings. Producers of other woody crops, such as nursery, citrus, and grapes, who use air-assisted sprayers, may be able to achieve similar savings by refining pesticide applications through the use of laser rangefinder-based spray application technology.

An Improved Method for the Calibration of a 2-D LiDAR With Respect to a Camera by Using a Checkerboard Target

This paper proposes an improved method for the calibration of a 2D LiDAR or a planar scanning laser rangefinder with respect to a camera, which uses the same checkerboard target as in the camera calibration. To address one of the drawbacks in the LiDAR calibration with a checkerboard, the sensitivity to checkerboard poses, laser points on the checkerboard are obtained from both the originally positioned LiDAR and the same LiDAR vertically rotated from the original position, in the proposed calibration. They construct the additional constraints with the normal vector of the checkerboard, and strengthen the basic point-on-plane constraints in the LiDAR calibration with a checkerboard. However, in practice, the LiDAR rotation can be imprecise. Thus, we further develop the calibration procedure and examine its calibration performance, in the presence of such impreciseness in the LiDAR rotation. A performance analysis is conducted with synthetic noisy data, and it is confirmed that the proposed calibration yields smaller error in the parameter estimation than a conventional baseline calibration method. Subsequently, we examine the calibration performance with real data, where the proposed calibration is carried out by building a calibration system. It is shown that the proposed calibration also has more consistency in the parameter estimation. For further study, we compare the proposed calibration based on point-on-plane constraints with a calibration based on point-to-point constraints, and show that the proposed calibration, with easier data acquisition, yields comparable results.

SCANNING MEASURING SYSTEM BASED ON TRIANGULAR LASER RANGEFINDER

A scanning laser range finder that provides distance measurement up to 10 m with an accuracy of 0.01 m for 360-degree circular scanning of space is described. Using the triangulation principle with a multi-element photodetector allows to creating a small-sized laser measuring system for robotic platforms for industrial and special purposes. Using the microcontroller allows to rationally implementing the discrete algorithm of the system in a given sector of space.

Extrinsic LiDAR/Ground Calibration Method Using 3D Geometrical Plane-Based Estimation.

This paper details a new extrinsic calibration method for scanning laser rangefinder that is precisely focused on the geometrical ground plane-based estimation. This method is also efficient in the challenging experimental configuration of a high angle of inclination of the LiDAR. In this configuration, the calibration of the LiDAR sensor is a key problem that can be be found in various domains and in particular to guarantee the efficiency of ground surface object detection. The proposed extrinsic calibration method can be summarized by the following procedure steps: fitting ground plane, extrinsic parameters estimation (3D orientation angles and altitude), and extrinsic parameters optimization. Finally, the results are presented in terms of precision and robustness against the variation of LiDAR’s orientation and range accuracy, respectively, showing the stability and the accuracy of the proposed extrinsic calibration method, which was validated through numerical simulation and real data to prove the method performance.

Intelligent Surrounding Recognition for Robot Direction Control

In this study, we present a novel robot navigation system based on multi-sensors. The sensors include laser range finder, electronic compass, Zig-bee module and camera, to construct the navigation and monitoring system for robot. This system can remote control robot and monitor its status by real-time images for security patrol in the room at any time. Based on sensing information, micro-controller can control motors to drive its moving directions and speed. Laser scanning rangefinder is used to allow the robot to detect obstacles by the viewing range of 0 to 240 degrees. Zig-Bee locator can provide relative indoor position information in a wide space. The electronic compass is adopted to control the moving direction of robot. The computer calculates the sensing data and its results are sent to the micro-controller, to enable the robot walking on the middle of the passageway. The system is successfully implemented and demonstrated in real environment.

A Simple Calibration Procedure for a 2D LiDAR With Respect to a Camera

This paper proposes a simple calibration procedure for a planar scanning laser rangefinder or a 2D LiDAR with respect to a regular monocular camera, in which a point-like target is utilized to obtain point-to-point correspondences, which can make the calibration problem simple. In the proposed method, a small spherical object is attached to a rod, which is hand-held, and the correspondences are simply and effectively acquired by setting the target in front of the sensors and repeatedly moving the target downward by hand, until the LiDAR captures the target. We also describe possible noise present in the proposed data acquisition process, followed by simulation and real experiment. The simulation study examines the performance of the proposed calibration in the presence of noise, in which the LiDAR parameter estimation is successful unless the noise is excessive. In the real experiment, the proposed calibration is conducted by constructing a real calibration system, where we evaluate the reprojection error of the proposed calibration. In both simulation and experiment, a conventional calibration procedure with a v-shaped board target is also conducted for evaluating the proposed calibration procedure. It is confirmed that the proposed method is more effective in point cloud acquisition and calibration performance.

Human-Machine Interface for a Smart Wheelchair

The paper describes the integration of hardware and software with sensor technology and computer processing to develop the next generation intelligent wheelchair. The focus is a computer cluster design to test high performance computing for smart wheelchair operation and human interaction. The LabVIEW cluster is developed for real-time autonomous path planning and sensor data processing. Four small form factor computers are connected over a Gigabit Ethernet local area network to form the computer cluster. Autonomous programs are distributed across the cluster for increased task parallelism to improve processing time performance. The distributed programs operating frequency for path planning and motion control is 50Hz and 12.3Hz for 0.3 megapixel robot vision system. To monitor the operation and control of the distributed LabVIEW code, network automation is integrated into the cluster software along with a performance monitor. A link between the computer motion control program and the wheelchair joystick control of the drive train is developed for the computer control interface. A perception sensor array and control circuitry is integrated with the computer system to detect and respond to the wheelchair environment. Multiple cameras are used for image processing and scanning laser rangefinder sensors for obstacle avoidance in the cluster program. A centralized power system is integrated to power the smart wheelchair along with the cluster and sensor feedback system. The on board computer system is evaluated for cluster processing performance for the smart wheelchair, incorporating camera machine vision and LiDAR perception for terrain obstacle detection, operating in urban scenarios.

Experimental Study of Motion of the Mobile Robots Moving in the Convoy Type Formation

In this paper the authors describe the experiment, which consists in studying the motion of the mobile robots moving in the convoy type formation, including one leader-robot and several follower-robots. The components of the educational robots and their sensor devices including Hokuyo urg-04lx-urg01 scanning laser rangefinder (only the leader-robot has it) are introduced. A distributed control system of the robots in ROS (Robot Operating System) is described, at the same time the functions of each node of the robots are presented in detail. The authors describe the SLAM algorithm of the leader-robot and its computational process in practice, and simultaneously the algorithm of the trajectory planning in real-time is described briefly. The authors present the PID controller for DC (direct current) motors and discuss the performance of DC motor according to its step response in the loading conditions. The motion control algorithm of the follower-robot is also presented. Experimental results including the map of environment and the trajectory of robots on the map are provided, which demonstrate effectiveness of the used algorithms. The authors analyze the reasons for discrepancy in the trajectories of some robots in a group, for example, different positioning accuracy of the leader-robot and follower-robots, delays in communication, insufficient performance of the actuator. The further research direction consists of two aspects: firstly, the coordinated movements of the drone and a group of mobile robots moving in the convoy type formation; secondly, logical control of a group of robots when changing the formation topology. The control mechanism ensured the execution of various convoy behaviors, for example, convoy separation.

Surface Estimation ICP Algorithm for Building a 3D Map by a Scanning LRF

A new three-dimensional (3D) map building method based on Laser Range Finder (LRF) has been proposed in this research, performing a surface estimation with the Iterative Closest Point (ICP) algorithm. While a mobile robot is navigating in an unknown environment, the entire environment cannot be scanned by LRF since kinematic features of the mobile robot and surface conditions are dynamically changing. To resolve this difficulty in building a 3D map while the mobile robot is navigating, a surface estimation ICP algorithm is proposed, which is based on the continuity of the environment around mobile robot. That is, this new algorithm recovers the un-scanned area by estimating feature points in the neighboring two regions based on the continuous environment information. The effectiveness of proposed algorithm has been demonstrated through real experiments of the mobile robot navigation.

Improving the trajectory tracking performance of autonomous orchard vehicles using wheel slip compensation

In this paper, the effects of wheel slip estimation and compensation of trajectory tracking for orchard applications were investigated. A slippage estimator was developed and adapted into a car-like robot model. Steering and velocity commands were generated using a model-based control approach. The whole system was implemented and tested on an autonomous orchard vehicle that has steerable front wheels and actuated rear wheels. A high accuracy positioning system was used to estimate the longitudinal and lateral slip velocities while the vehicle is moving. A laser scanning range finder was placed at the front centre of the vehicle, which was used to detect rows of trees in the orchard. Procedures were first tested in a non-flat but open space, which was covered with snow. Then it was tested on an experimental orchard where the surface was covered with heavy mud and the vehicle was expected to follow trajectories that span multiple rows in the orchard. The vehicle detected individual trees as well as rows of trees to track the centre of each row and manoeuvred from one row to the next. The experimental results showed that trajectory tracking performance of the vehicle was enhanced via integrating a slippage estimator into the system model. Furthermore, using the slippage estimation in the system model increased the accuracy, repeatability and performance of the control system.

UAV-BASED ACQUISITION OF 3D POINT CLOUD – A COMPARISON OF A LOW-COST LASER SCANNER AND SFM-TOOLS

Abstract. The Project ADFEX (Adaptive Federative 3D Exploration of Multi Robot System) pursues the goal to develop a time- and cost-efficient system for exploration and monitoring task of unknown areas or buildings. A fleet of unmanned aerial vehicles equipped with appropriate sensors (laser scanner, RGB camera, near infrared camera, thermal camera) were designed and built. A typical operational scenario may include the exploration of the object or area of investigation by an UAV equipped with a laser scanning range finder to generate a rough point cloud in real time to provide an overview of the object on a ground station as well as an obstacle map. The data about the object enables the path planning for the robot fleet. Subsequently, the object will be captured by a RGB camera mounted on the second flying robot for the generation of a dense and accurate 3D point cloud by using of structure from motion techniques. In addition, the detailed image data serves as basis for a visual damage detection on the investigated building. This paper focuses on our experience with use of a low-cost light-weight Hokuyo laser scanner onboard an UAV. The hardware components for laser scanner based 3D point cloud acquisition are discussed, problems are demonstrated and analyzed, and a quantitative analysis of the accuracy potential is shown as well as in comparison with structure from motion-tools presented.

A Model-Based Joint Detection and Tracking Approach for Multi-Vehicle Tracking With Lidar Sensor

This paper presents a method for joint detection and tracking of vehicles with a scanning laser rangefinder. The lidar measurements of an object have the particularity to be spatially distributed, which generally leads to a detection step before any tracking. Differently, the proposed method relies on the raw measurement processing without any detection step, which improves the overall performance in multiobject tracking while providing good estimation accuracies. The solution uses the sequential Monte Carlo methods by incorporating the geometric invariant of the objects of interest (vehicles). This approach also offers an efficient solution to the problem of multitarget tracking by integrating naturally the track management in the filtering process.