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The most prominent example of scan matching algorithm is the Iterative Closest Point (ICP) algorithm. But the ICP algorithm and its variants excessively depend on the initial pose estimate between two scans. The purpose of this paper is to propose a scan matching algorithm, which is adaptable to big initial pose errors.

The environments are represented by flat units and upright units. The upright units are clustered to represent objects that the robot cannot cross over. The object cluster is further discretized to generate layered model consisting of cross-section ellipses. The layered model provides simplified features that facilitate an object recognition algorithm to discriminate among common objects in outdoor environments. A layered model graph is constructed with the recognized objects as nodes. Based on the similarity of sub-graphs in each scans, the layered model graph-based matching algorithm generates initial pose estimates and uses ICP to refine the scan matching results.

Experimental results indicate that the proposed algorithm can deal with bad initial pose estimates and increase the processing speed. Its computation time is short enough for real-time implementation in robotic applications in outdoor environments.

This paper proposes a bio-inspired scan matching algorithm for mobile robots based on layered model graph in outdoor environments.

The purpose of this paper is to demonstrate a real time 3D localization and mapping approach for the USAR (Urban Search and Rescue) robotic application, focusing on the performance and the accuracy of the General‐purpose computing on graphics processing units (GPGPU)‐based iterative closest point (ICP) 3D data registration implemented using modern GPGPU with FERMI architecture.

The authors put all the ICP computation into GPU, and performed the experiments with registration up to 106 data points. The main goal of the research was to provide a method for real‐time data registration performed by a mobile robot equipped with commercially available laser measurement system 3D. The main contribution of the paper is a new GPGPU based ICP implementation with regular grid decomposition. It guarantees high accuracy as equivalent CPU based ICP implementation with better performance.

The authors have shown an empirical analysis of the tuning of GPUICP parameters for obtaining much better performance (acceptable level of the variance of the computing time) with minimal lost of accuracy. Loop closing method is added and demonstrates satisfactory results of 3D localization and mapping in urban environments. This work can help in building the USAR mobile robotic applications that process 3D cloud of points in real time.

This work can help in developing real time mapping for USAR robotic applications.

The paper proposes a new method for nearest neighbor search that guarantees better performance with minimal loss of accuracy. The variance of computational time is much less than SoA.

This paper aims to propose an identification method based on monocular vision for cylindrical parts in cluttered scene, which solves the issue that iterative closest point (ICP) algorithm fails to obtain global optimal solution, as the deviation from scene point cloud to target CAD model is huge in nature.

The images of the parts are captured at three locations by a camera amounted on a robotic end effector to reconstruct initial scene point cloud. Color signatures of histogram of orientations (C-SHOT) local feature descriptors are extracted from the model and scene point cloud. Random sample consensus (RANSAC) algorithm is used to perform the first initial matching of point sets. Then, the second initial matching is conducted by proposed remote closest point (RCP) algorithm to make the model get close to the scene point cloud. Levenberg Marquardt (LM)-ICP is used to complete fine registration to obtain accurate pose estimation.

The experimental results in bolt-cluttered scene demonstrate that the accuracy of pose estimation obtained by the proposed method is higher than that obtained by two other methods. The position error is less than 0.92 mm and the orientation error is less than 0.86°. The average recognition rate is 96.67 per cent and the identification time of the single bolt does not exceed 3.5 s.

The presented approach can be applied or integrated into automatic sorting production lines in the factories.

The proposed method improves the efficiency and accuracy of the identification and classification of cylindrical parts using a robotic arm.

This paper aims to present the spherical entropy image (SEI), a novel global descriptor for the scan registration of three-dimensional (3D) point clouds. This paper also introduces a global feature-less scan registration strategy based on SEI. It is advantageous for 3D data processing in the scenarios such as mobile robotics and reverse engineering.

The descriptor works through representing the scan by a spherical function named SEI, whose properties allow to decompose the six-dimensional transformation into 3D rotation and 3D translation. The 3D rotation is estimated by the generalized convolution theorem based on the spherical Fourier transform of SEI. Then, the translation recovery is determined by phase only matched filtering.

No explicit features and planar segments should be contained in the input data of the method. The experimental results illustrate the parameter independence, high reliability and efficiency of the novel algorithm in registration of feature-less scans.

A novel global descriptor (SEI) for the scan registration of 3D point clouds is presented. It inherits both descriptive power of signature-based methods and robustness of histogram-based methods. A high reliability and efficiency registration method of scans based on SEI is also demonstrated.

Laser navigation without a reflector does not require setup of reflector markers at the scene and thus has the advantages of free path setting and flexible change. This technology has attracted wide attention in recent years and shows great potential in the field of automatic logistics, including map building and locating in real-time according to the environment. This paper aims to focus on the application of feature matching for map building.

First, an improved linear binary relation algorithm was proposed to calculate the local similarity of the feature line segments, and the matching degree matrix of feature line segments between two adjacent maps was established. Further, rough matching for the two maps was performed, and both the initial rotation matrix and the translation vector for the adjacent map matching were obtained. Then, to improve the rotation matrix, a region search optimization algorithm was proposed, which took the initial rotation matrix as the starting point and searched gradually along a lower error-of-objective function until the error sequence was nonmonotonic. Finally, the random-walk method was proposed to optimize the translation vector by iterating until the error-objective function reached the minimum.

The experimental results show that the final matching error was controlled within 10 mm after both rotation and translation optimization. Also, the algorithm of map matching and optimization proposed in this paper can realize accurately the feature matching of a laser navigation map and basically meet the real-time navigation and positioning requirements for an automated-guided robot.

A linear binary relation algorithm was proposed, and the local similarity between line segments is calculated on the basis of the binary relation. The hill-climbing region search algorithm and the random-walk algorithm were proposed to optimize the rotation matrix and the translation vector, respectively. This algorithm has been applied to industrial production.

The purpose of this paper is to propose an imperfect symmetry transform framework for orbital prosthesis modelling.

Current models of patients with orbital defects have imperfect symmetries. Commonly used methods, such as principal component analysis (PCA) or iterative closest points algorithm (ICP), do not detect perfect symmetries and therefore produce poor results. The authors propose an improved ICP algorithm based on the M‐estimator, which can remove outliers from the optimization and detect incorrect symmetry. Using this algorithm, the mid‐facial plane of a patient's facial model can be precisely obtained despite perturbation of the facial shape due to the defect.

The results showed that the orbital prosthesis fitted well to the patient's appearance. Clinical applications confirmed that this framework is attractive and has the potential for use in creating desired orbital prostheses or other bilateral maxillofacial prostheses in daily clinical practice.

The method described in this report will improve the fabrication accuracy of orbital prostheses or other bilateral maxillofacial prostheses.

This imperfect symmetry transform framework has great potential for use in clinical applications because of its advantages over other existing methods in terms of accuracy.

The purpose of this study was to solve the problem of pose measurement of various parts for a precision assembly system.

A novel alignment method which can achieve high-precision pose measurement of microparts based on monocular microvision system was developed. To obtain the precise pose of parts, an area-based contour point set extraction algorithm and a point set registration algorithm were developed. First, the part positioning problem was transformed into a probability-based two-dimensional point set rigid registration problem. Then, a Gaussian mixture model was fitted to the template point set, and the contour point set is represented by hierarchical data. The maximum likelihood estimate and expectation-maximization algorithm were used to estimate the transformation parameters of the two point sets.

The method has been validated for accelerometer assembly on a customized assembly platform through experiments. The results reveal that the proposed method can complete letter-pedestal assembly and the swing piece-basal part assembly with a minimum gap of 10 µm. In addition, the experiments reveal that the proposed method has better robustness to noise and disturbance.

Owing to its good accuracy and robustness for the pose measurement of complex parts, this method can be easily deployed to assembly system.

In this paper, an attempt has been made to develop an algorithm equipped with geometric pattern registration techniques to perform exact, robust and fast robot localization purely based on laser range data.

The expected pose of the robot on a pre-calculated map is in the form of simulated sensor readings. To obtain the exact pose of the robot, segmentation of both real laser range and simulated laser range readings is performed. Critical points on two scan sets are extracted from the segmented range data and thereby the pose difference is computed by matching similar parts of the scans and calculating the relative translation.

In contrast to other self-localization algorithms based on particle filters and scan matching, the proposed method, in common positioning scenarios, provides a linear cost with respect to the number of sensor particles, making it applicable to real-time resource-limited embedded robots. The proposed method is able to obtain a sensibly accurate estimate of the relative pose of the robot even in non-occluded but partially visible segments conditions.

A comparison of state-of-the-art localization techniques has shown that geometrical scan registration algorithm is superior to the other localization methods based on scan matching in accuracy, processing speed and robustness to large positioning errors. Effectiveness of the proposed method has been demonstrated by conducting a series of real-world experiments.

This paper aims to focus on real-world mobile systems, and thus propose relevant contribution to the special issue on “Real-world mobile robot systems”. This work on 3D laser semantic mobile mapping and particle filter localization dedicated for robot patrolling urban sites is elaborated with a focus on parallel computing application for semantic mapping and particle filter localization. The real robotic application of patrolling urban sites is the goal; thus, it has been shown that crucial robotic components have reach high Technology Readiness Level (TRL).

Three different robotic platforms equipped with different 3D laser measurement system were compared. Each system provides different data according to the measured distance, density of points and noise; thus, the influence of data into final semantic maps has been compared. The realistic problem is to use these semantic maps for robot localization; thus, the influence of different maps into particle filter localization has been elaborated. A new approach has been proposed for particle filter localization based on 3D semantic information, and thus, the behavior of particle filter in different realistic conditions has been elaborated. The process of using proposed robotic components for patrolling urban site, such as the robot checking geometrical changes of the environment, has been detailed.

The focus on real-world mobile systems requires different points of view for scientific work. This study is focused on robust and reliable solutions that could be integrated with real applications. Thus, new parallel computing approach for semantic mapping and particle filter localization has been proposed. Based on the literature, semantic 3D particle filter localization has not yet been elaborated; thus, innovative solutions for solving this issue have been proposed. Recently, a semantic mapping framework that was already published was developed. For this reason, this study claimed that the authors’ applied studies during real-world trials with such mapping system are added value relevant for this special issue.

The main problem is the compromise between computer power and energy consumed by heavy calculations, thus our main focus is to use modern GPGPU, NVIDIA PASCAL parallel processor architecture. Recent advances in GPGPUs shows great potency for mobile robotic applications, thus this study is focused on increasing mapping and localization capabilities by improving the algorithms. Current limitation is related with the number of particles processed by a single processor, and thus achieved performance of 500 particles in real-time is the current limitation. The implication is that multi-GPU architectures for increasing the number of processed particle can be used. Thus, further studies are required.

The research focus is related to real-world mobile systems; thus, practical aspects of the work are crucial. The main practical application is semantic mapping that could be used for many robotic applications. The authors claim that their particle filter localization is ready to integrate with real robotic platforms using modern 3D laser measurement system. For this reason, the authors claim that their system can improve existing autonomous robotic platforms. The proposed components can be used for detection of geometrical changes in the scene; thus, many practical functionalities can be applied such as: detection of cars, detection of opened/closed gate, etc. […] These functionalities are crucial elements of the safe and security domain.

Improvement of safe and security domain is a crucial aspect of modern society. Protecting critical infrastructure plays an important role, thus introducing autonomous mobile platforms capable of supporting human operators of safe and security systems could have a positive impact if viewed from many points of view.

This study elaborates the novel approach of particle filter localization based on 3D data and semantic mapping. This original work could have a great impact on the mobile robotics domain, and thus, this study claims that many algorithmic and implementation issues were solved assuming real-task experiments. The originality of this work is influenced by the use of modern advanced robotic systems being a relevant set of technologies for proper evaluation of the proposed approach. Such a combination of experimental hardware and original algorithms and implementation is definitely an added value.

The purpose of this study is to present a new and relatively inexpensive method for posture evaluation of the positioning of the wing-body assembly. Positioning is an essential process to guarantee alignment accuracy in an assembly line.

The studied method includes a structural set-up and a software algorithm used to process a set of experimental input data to compute the actual position of the wing with respect to the ideal position, which is proposed considering measurement uncertainty, the deviation caused by large errors in measurement points and the different tolerance requirements.

The studied method has been found to be simple and effective in addition to being highly accurate. Compared with most of the current methods that have been developed with optical equipment, it is more cost- and space-efficient. The automation process determines how much operation time will be saved.

The studied method has been applied in an actual assembly line, and the economic and time savings illustrate its benefits.

This method provides an attractive wing-body assembly solution for those enterprises that want to find a low-cost option or have limited measuring space for optical equipment. It can also be the basis for the accurate assembly of other large parts for aircraft and other vessels.