Laser Range Finder Data Research Articles

Construction of a semi‐dense point cloud model for a tube‐to‐tubesheet welding robot

Tube-to-tubesheet welding is widely applied in industrial fields. However, the current tubesheet welding robot still mainly relies on manual tubesheet models. Aiming to solve this problem, this paper proposed an improved direct method to automatically establish a tubesheet semi-dense point cloud model based on a selected monocular camera and a one-dimension (1D) laser rangefinder. Firstly, a laser filtering method was designed to acquire the distance between the camera and tubesheet through the 1D laser rangefinder. Then, from combing the 1D laser rangefinder data with keyframe data, the scale factor was obtained and proceeded to be processed by the Kalman filter to reduce the error. Then, the computed scale factor and all the keyframes were calculated to construct the tubesheet point cloud model through the graph optimisation algorithm. The experimental results showed that the semi-dense point cloud model of the tubesheet could be efficiently established by the proposed algorithm with row error and column error both less than 1 mm, satisfying the welding requirements.

Effective Safety Strategy for Mobile Robots Based on Laser-Visual Fusion in Home Environments

The proven efficacy of safety strategies based on 2-D laser rangefinder (LRF) strongly stimulates their application to mobile robots operating in the home environment. However, it remains a challenge for the robot to avoid collisions with all obstacles in the environment. Since LRF can only scan a horizontal slice of the world, some objects cannot be fully observed, such as tables and chairs. In this article, an effective solution based on laser-visual fusion is presented to enhance the safety of the robot. First, a vision sensor is adopted to help detect obstacles that are not fully visible to LRF. Then we propose a method to convert the depth information of the visual image into 2-D <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pseudo-laser data</i> representation. With this representation, a strategy for 2-D mapping is developed. On this basis, a novel map fusion algorithm is proposed to generate an improved grid map that amends the incorrect representation of obstacles on the traditional 2-D grid map. We further investigate a robot autonomous navigation strategy that considers LRF data and pseudo-laser data to avoid all obstacles. Experimental results show that the improved grid map together with the presented navigation strategy allows the robot not only to plan a “real” collision-free path, but also to navigate safely in both static and dynamic scenarios, and the proposed strategies can significantly enhance the performance of robot navigation in terms of safety, reliability and robustness.

A Relative Pose Estimation Method of Non-Cooperative Space Targets

In many space missions such as fly-around observing and approaching the targets, the relative pose estimation of non-cooperative space targets is one of the key technologies. In this paper, a relative pose estimation method of non-cooperative space targets based on a monocular camera and a laser rangefinder is proposed. The monocular camera is used to obtain the sequence image of the target. The laser rangefinder is used to solve the scale fuzziness problem of the monocular camera and construct the world coordinate system in real scale during initialization. The camera data and the laser rangefinder data are fused in a tightly coupled form to optimize the estimated pose in continuous pose estimation. The non-cooperative space target images generated by Blender are used for simulation. The results show that the method proposed in this paper has good real-time performance and can estimate the relative pose of non-cooperative space targets accurately and robustly. Compared with the existing methods based on monocular vision, the proposed method does not require the initial pose assumption and can effectively improve the accuracy of the pose estimation.

A Method of Multiple Dynamic Objects Identification and Localization Based on Laser and RFID.

In an indoor environment, object identification and localization are paramount for human-object interaction. Visual or laser-based sensors can achieve the identification and localization of the object based on its appearance, but these approaches are computationally expensive and not robust against the environment with obstacles. Radio Frequency Identification (RFID) has a unique tag ID to identify the object, but it cannot accurately locate it. Therefore, in this paper, the data of RFID and laser range finder are fused for the better identification and localization of multiple dynamic objects in an indoor environment. The main method is to use the laser range finder to estimate the radial velocities of objects in a certain environment, and match them with the object’s radial velocities estimated by the RFID phase. The method also uses a fixed time series as “sliding time window” to find the cluster with the highest similarity of each RFID tag in each window. Moreover, the Pearson correlation coefficient (PCC) is used in the update stage of the particle filter (PF) to estimate the moving path of each cluster in order to improve the accuracy in a complex environment with obstacles. The experiments were verified by a SCITOS G5 robot. The results show that this method can achieve an matching rate of 90.18% and a localization accuracy of 0.33m in an environment with the presence of obstacles. This method effectively improves the matching rate and localization accuracy of multiple objects in indoor scenes when compared to the Bray-Curtis (BC) similarity matching-based approach as well as the particle filter-based approach.

Robot path planning based on laser range finder and novel objective functions in grey wolf optimizer

Mobile robots are the robots that can move through the environment and be used in many applications, including the industrial environment, planet exploration, warehousing, and daily household chores. They can be controlled by an operator, set to do some specific jobs, or work autonomously. Robot path planning is the task of an autonomous robot to move safely from one position to another. In this paper, three new objective functions are introduced in the structure of improved grey wolf optimizer (IGWO) and improved particle swarm optimization (IPSO) for the robot path planning problems. As another part of our proposed method, a reduction of laser range finder (LRF) data is performed, and the avoidance collision approach is also introduced. Robots determine the next position by using LRF data and IGWO (IPSO) algorithms in a local approach. The initial and the goal positions are predefined for each robot. Moreover, the location of static obstacles and other robots are unknown for each robot. Finally, the experimental results of the robot path planning using IGWO are compared to different algorithms. The results indicate that the proposed method performs better in determining an optimal, short, safe, and smooth path. Also, it has less power and time consumption than other methods. All the algorithms are implemented in the V-REP robot simulator.

Improved auto-extrinsic calibration between stereo vision camera and laser range finder

ABSTRACT This study identifies a way to accurately estimate extrinsic calibration parameters between stereo vision camera and 2D laser range finder (LRF) based on 3D reconstruction of monochromatic calibration board and geometric co-planarity constraints between the views from these two sensors. It supports automatic extraction of plane-line correspondences between camera and LRF using monochromatic board, which is further improved by selecting optimal threshold values for laser scan dissection to extract line features from LRF data. Calibration parameters are then obtained by solving co-planarity constraints between the estimated plane and line. Furthermore, the obtained parameters are refined by minimising reprojection error and error from the co-planarity constraints. Moreover, calibration accuracy is achieved because of extraction of reliable plane-line correspondence using monochromatic board which reduces the impact of range-reflectivity-bias observed in LRF data on checkerboard . As the proposed method supports to automatically extract feature correspondences, it provides a major reduction in time required from an operator in comparison to manual methods. The performance is validated by extensive experimentation and simulation, and estimated parameters from the proposed method demonstrate better accuracy than conventional methods.

The Extrinsic Calibration of Area-Scan Camera and 2D Laser Rangefinder (LRF) Using Checkerboard Trihedron

The combination of area-scan camera and 2D laser rangefinder (LRF) can capture both textural and geometrical information from a scene at the same time, and has been widely used in various fields. Due to the differences of the installation position and acquisition mode, calibrating the extrinsic parameters, including the rotation and translation, of two sensors is necessary for fusing the camera image and LRF data. In this paper, a simple and flexible extrinsic calibration method is proposed by only acquiring a checkerboard trihedron once. Using checkerboard trihedron as a mobile referenced control field, the proposed method includes three steps to calibrate the extrinsic parameters. First, the rotation and translation between the trihedron and LRF are solved with a simplified perspective-three-point (P3P) solution; Second, using the collinear equation of checkerboard corners and their pixels in the image, the camera is calibrated with respect to the trihedron; Third, combining the last two steps, the rotation and translation parameters between the camera and LRF are finally calibrated with the intermediate referenced trihedron. After a lot of simulation and real experiments, the proposed method has been demonstrated to have the advantages of simple operation, strong robustness and high accuracy in real experiment.

Vision‐Based Three‐Dimensional Reconstruction and Monitoring of Large‐Scale Steel Tubular Structures

A four‐ocular vision system is proposed for the three‐dimensional (3D) reconstruction of large‐scale concrete‐filled steel tube (CFST) under complex testing conditions. These measurements are vitally important for evaluating the seismic performance and 3D deformation of large‐scale specimens. A four‐ocular vision system is constructed to sample the large‐scale CFST; then point cloud acquisition, point cloud filtering, and point cloud stitching algorithms are applied to obtain a 3D point cloud of the specimen surface. A point cloud correction algorithm based on geometric features and a deep learning algorithm are utilized, respectively, to correct the coordinates of the stitched point cloud. This enhances the vision measurement accuracy in complex environments and therefore yields a higher‐accuracy 3D model for the purposes of real‐time complex surface monitoring. The performance indicators of the two algorithms are evaluated on actual tasks. The cross‐sectional diameters at specific heights in the reconstructed models are calculated and compared against laser rangefinder data to test the performance of the proposed algorithms. A visual tracking test on a CFST under cyclic loading shows that the reconstructed output well reflects the complex 3D surface after correction and meets the requirements for dynamic monitoring. The proposed methodology is applicable to complex environments featuring dynamic movement, mechanical vibration, and continuously changing features.

Multiple Vehicle Tracking Based on Labeled Multiple Bernoulli Filter Using Pre-Clustered Laser Range Finder Data

Multiple vehicle tracking (MVT) system is a prerequisite to path planning and decision making of self-driving cars as it can provide positions of surrounding vehicles. Most of the available approaches belonging to the so called tracking-by-detection approach inevitably bring detection errors into the tracking result. In this study, we proposed a laser range finder (LRF) based track-before-detect MVT algorithm without detection procedure. Moreover, different from the state of the art in track-before-detect approaches using raw data, we applied a pre-clustering procedure to segment the raw data into disjoint clusters to reduce computation demand. Specifically, a clustering algorithm named iterative nearest point search (INPS) which can even handle the partial occlusion situations that are challenging for traditional clustering algorithms was designed for the pre-clustering procedure. Furthermore, a detailed cluster-to-target measurement model was proposed to describe the difference between cluster and hypothesis vehicle. Finally, we integrated the measurement model into the labeled multi-Bernoulli filter with particle implementation. Simulations and experiments show that the proposed MVT algorithm provides more accurate estimates of vehicle number and position in comparison with conventional methods.

A Box Particle Filter Method for Tracking Multiple Extended Objects

Extended objects generate a variable number of multiple measurements. In contrast with point targets, extended objects are characterized with their size or volume, and orientation. Multiple object tracking is a notoriously challenging problem due to complexities caused by data association. This paper develops a box particle filter (box PF) method for multiple extended object tracking, and for the first time, it is shown how interval-based approaches can deal efficiently with data association problems and reduce the computational complexity of the data association. The box PF relies on the concept of a box particle. A box particle represents a random sample and occupies a controllable rectangular region of nonzero volume in the object state space. A theoretical proof of the generalized likelihood of the box PF for multiple extended objects is given based on a binomial expansion. Next, the performance of the box PF is evaluated using a challenging experiment with the appearance and disappearance of objects within the area of interest, with real laser rangefinder data. The box PF is compared with a state-of-the-art particle filter with point particles. Accurate and robust estimates are obtained with the box PF, both for the kinematic states and extent parameters, with significant reductions in computational complexity. The box PF reduction of computational time is atleast 32% compared with the particle filter working with point particles for the experiment presented. Another advantage of the box PF is its robustness to initialization uncertainty.

Real-time detection of surface deformation and strain in recycled aggregate concrete-filled steel tubular columns via four-ocular vision

• A mark-free vision-based dynamic real-time measurement technique is proposed for full field strain and 3D deformation detection. • Point cloud combination and 3D reconstruction are applied. • The non-contact method results truly reflect the deformation process and full-field strain values. • The standard deviation mean value measured by the proposed method relative to the true value is 1.23%. • The proposed method can be used as a reasonable alternative to traditional contact methods. This study presents a dynamic real-time detection method for surface deformation and full field strain in recycled aggregate concrete-filled steel tubular columns (RACSTCs). Automatic calibration and dynamic surface tracking measurement are utilized and mathematical models combining the four-ocular visual coordinates and point cloud matching are proposed. The 3D deformation of the RACSTCs under low cyclic loading are gathered every 60 s as sample images. The four-ocular vision system constitutes two groups of binocular stereoscopic vision systems. The 3D deformation surface is reconstructed by multi-ocular vision coordinate association, image preprocessing, and point cloud registration. The measurement results are validated by comparison against laser range finder data. The standard deviation mean value of 10 specimens measured by the proposed method relative to the true value is 1.23%; the mean error of the maximum absolute value is 2.82%.

Improving SLAM by Exploiting Building Information from Publicly Available Maps and Localization Priors

Maps are needed for a wide range of applications. In the context of mobile robotics, the map learning problem under uncertainty is often referred to as the simultaneous localization and mapping problem. In this paper, we aim at exploiting already available information such as OpenStreetMap data within the Simultaneous Localization and Mapping (SLAM) problem. We achieve this by relating the information about buildings with the perceptions of the robot and generate constraints for the pose graph-based formulation of the SLAM problem. In addition to that, we present a way to select target locations for the robot so that by going there, the robot can expect to reduce its own pose uncertainty. This localizability information is generated directly from OpenStreetMap data and supports active localization. We implemented and evaluated our approach using real-world data taken in urban environments. Our experiments suggest that we are able to relate the newly built maps with information from OpenStreetMap with the laser range finder data from the robot and in this way improve the map quality. The extension to graph-based SLAM provides better aligned maps and adds only a marginal computational overhead. Furthermore, we illustrate that the localizability information is useful to evaluate the ability to localize the robot given a trajectory.

Human-like Artificial Intelligent Wheelchair Robot Navigated by Multi-Sensor Models in Indoor Environments and Error Analysis

Intelligent mobile robot navigation in indoor environments is still a challenge. In this paper, we propose a method in which the wheelchair robot imitates human like navigation by interacting with the surrounding environments. Two types of sensor data are used to train neural networks, which are later used to control the robot to reach the goal location in different indoor environments. The robot navigates from the start to the goal location in the environments with obstacles. In first model, we used the Laser Range Finder (LRF) sensor data as input of the neural network. In the second model in addition to the LRF data, the processed camera sensor data are also utilized. We compare the performance of two neural networks models by analyzing the error between the human and neural network based real robot navigations. The experimental results show that our proposed models are efficient for mobile robot navigations. In addition, errors are analyzed in this paper.

Mobile robot self-localization in unstructured environments based on observation localizability estimation with low-cost laser range-finder and RGB-D sensors

When service robots work in human environments, unexpected and unknown moving people may deteriorate the convergence of robot localization or even cause failure localization if the environment is crowded. In this article, a multisensor observation localizability estimation method is proposed and implemented for supporting reliable robot localization in unstructured environments with low-cost sensors. The contribution of the approach is a strategy that combines noisy laser range-finder data and RGB-D data for estimating the dynamic localizability matrix in a probabilistic framework. By aligning two sensor frames, the unreliable part of the laser readings that hits unexpected moving people is fast extracted according to the output of a RGB-D-based human detector, so that the influence of unexpected moving people on laser observations can be explicitly factored out. The method is easy for implementation and is highly desirable to ensure robustness and real-time performance for long-term operation in populated environments. Comparative experiments are conducted and the results confirm the effectiveness and reliability of the proposed method in improving the localization accuracy and reliability in dynamic environments.

비 가시 환경에서의 LRF와 CCD 카메라의 성능비교

In this paper, range measurement performance of LRF (Laser Range Finder) module and image contrast of color CCD camera are evaluated under the aerosol (high temperature steam) environments, which are simulated severe accident conditions of the LWR (Light-Water-Reactor) nuclear power plant. Data of LRF and color CCD camera are key informations, which are needed in the implementation of SLAM (Simultaneous Localization and Mapping) function for emergency response robot system to cope with urgently accidents of the nuclear power plant.

A Dynamic Local Path Planning Method for Outdoor Robot Based on Characteristics Extraction of Laser Rangefinder and Extended Support Vector Machine

For dynamic path planning problem under unstructured environment, firstly, successive edge following and least squares method (SEF-LSM) is adopted to extract environment characteristics of laser rangefinder data, and SEF-LSM with logical reasoning (SEF-LSM-LR) is proposed for dynamic obstacles characteristics detection. Furthermore, the perpendicularity (PERP) algorithm is utilized to identify dynamic vehicle, according to the perpendicularity attribute of vehicle. Secondly, all the laser rangefinder scanning points are marked as negative ([Formula: see text]) or positive ([Formula: see text]1), and the scanning points of one dynamic obstacle are marked as the same label. Thirdly, extended support vector machine (ESVM) is designed for outdoor robot local path planning under unstructured environment, which consider the practical start-goal position and heading constraints, robot kinematic constraint, and curvature constraint, moreover, the emergency obstacle is regarded as disturbances during planning processing. Finally, the optimal path is chosen by the shortest distance evaluation function. Lots of outdoor simulations show that the proposed method solve the dynamic planning problem under unstructured environment, and their effectiveness performance are verified for outdoor robot path planning.

Multisensor Information Fusion for People Tracking With a Mobile Robot: A Particle Filtering Approach

People tracking based on multisensor information fusion is addressed. A framework is presented for fusing the laser range finder (LRF) data and the monocular camera data. Based on this framework, an LRF-based detection algorithm is proposed to identify the pairs of human legs, by combining motion information and metric features. Moreover, a geometric observation model is developed for the camera to extract both the range and bearing measurements of the target person by focusing the target’s shoes with the camera. Then, a near-optimal particle filter is designed to fuse the measurements from the LRF and the camera. To prevent the sample impoverishment, a procedure of sample diversity improvement is used after the resampling step. The full occlusion problem is solved using image matching based on speeded up robust feature. Note that either of the LRF and the camera can work independently, since both the range and bearing are simultaneously acquired from the LRF or the camera. As a result, flexible and robust tracking can be achieved. Extensive experiments demonstrate that the proposed approach achieves high tracking accuracy and robustness. Especially, only a very small number of particles suffice to maintain good tracking performance.

3D Laser Imaging Reconstruction of Scenes Using New Photometric Gaussian Beam Range Equation

In this article the problem of three-dimensional (3-D) reconstruction of scenes from terrestrial laser rangefinder data is processed, we present the simulations results of radiometric range function of the laser range finder imaging system for better optimization of maximum range detection with a given SNR (Signal-to-noise ratio). The study proposes a new reformulation of the radiometric Gaussian beam range equation on the basis of the photodetector active area. The adjustment of the optical and geometrical characteristics of the laser range finder so defined can be used to minimize the signal span to avoid the saturation of the detector cell and improve the ranging measurements (accuracy and range) for a 3D imaging application.

Dynamic Track Management in MHT for Pedestrian Tracking Using Laser Range Finder

Real time pedestrian tracking could be one of the important features for autonomous navigation. Laser Range Finder (LRF) produces accurate pedestrian data but a problem occurs when a pedestrian is represented by multiple clusters which affect the overall tracking process. Multiple Hypothesis Tracking (MHT) is a proven method to solve tracking problem but suffers a large computational cost. In this paper, a multilevel clustering of LRF data is proposed to improve the accuracy of a tracking system by adding another clustering level after the feature extraction process. A Dynamic Track Management (DTM) is introduced in MHT with multiple motion models to perform a track creation, association, and deletion. The experimental results from real time implementation prove that the proposed multiclustering is capable of producing a better performance with less computational complexity for a track management process. The proposed Dynamic Track Management is able to solve the tracking problem with lower computation time when dealing with occlusion, crossed track, and track deletion.

Development of a Laser-Range-Finder-Based Human Tracking and Control Algorithm for a Marathoner Service Robot

This paper presents a human detection algorithm and an obstacle avoidance algorithm for a marathoner service robot (MSR) that provides a service to a marathoner while training. To be used as a MSR, the mobile robot should have the abilities to follow a running human and avoid dynamically moving obstacles in an unstructured outdoor environment. To detect a human by a laser range finder (LRF), we defined features of the human body in LRF data and employed a support vector data description method. In order to avoid moving obstacles while tracking a running person, we defined a weighted radius for each obstacle using the relative velocity between the robot and an obstacle. For smoothly bypassing obstacles without collision, a dynamic obstacle avoidance algorithm for the MSR is implemented, which directly employed a real-time position vector between the robot and the shortest path around the obstacle. We verified the feasibility of these proposed algorithms through experimentation in different outdoor environments.