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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 describe a novel application of the well‐established 2D laser scan‐matching technique for self‐localization of a walking robot. The techniques described in this paper enable a walking robot with a 2D laser scanner to obtain precise maps of man‐made environments, which can be useful in search and reconnaissance missions, e.g. in warehouses, production plants, and other industrial areas.

The presented system combines two scan‐matching algorithms (PSM and PLICP) to deal with low‐quality range data from a compact laser scanner and to provide robust self‐localization in various types of man‐made environments. Data from proprioceptive sensors and simplifying assumptions holding in man‐made environments are exploited to compensate for the varying attitude of the walking robot, particularly in uneven terrain.

The experimental results suggest that neglecting either the poor initial pose guess obtained from the legged odometry, or the varying attitude angles of a walking robot's body, may lead to unacceptable results in self‐localization and scan‐based mapping. It is also demonstrated that using the PSM algorithm to compute the initial pose estimate for the more precise PLICP scan‐matching algorithm improves the results of self‐localization.

So far, the presented self‐localization system was tested in limited‐scale indoor experiments. Experiments with more extended and realistic scenarios are scheduled as further work.

Applying techniques described in this paper, the author was able to obtain the robot pose and precise maps of man‐made environments, which can be useful in USAR and reconnaissance missions, also in warehouses, production plants, and other industrial areas.

The scan‐matching algorithms used in the presented research are not new, the contribution lies in combining them in order to overcome issues specific to a small‐size legged platform, using only common affordable hardware.

The purpose of this paper is to present a scan matching simultaneous localization and mapping (SLAM) algorithm based on particle filter to generate the grid map online. It mainly focuses on reducing the memory consumption and alleviating the loop closure problem.

The proposed method alleviates the loop closure problem by improving the accuracy of the robot’s pose. First, two improvements were applied to enhance the accuracy of the hill climbing scan matching. Second, a particle filter was used to maintain the diversity of the robot’s pose and then to supply potential seeds to the hill climbing scan matching to ensure that the best match point was the global optimum. The proposed method reduces the memory consumption by maintaining only a single grid map.

Simulation and experimental results have proved that this method can build a consistent map of a complex environment. Meanwhile, it reduced the memory consumption and alleviates the loop closure problem.

In this paper, a new SLAM algorithm has been proposed. It can reduce the memory consumption and alleviate the loop closure problem without lowering the accuracy of the generated grid map.

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.

An efficient perception of the complex environment is the foremost requirement in mobile robotics. At present, the utilization of glass as a glass wall and automated transparent door in the modern building has become a highlight feature for interior decoration, which has resulted in the wrong perception of the environment by various range sensors. The perception generated by multi-data sensor fusion (MDSF) of sonar and laser is fairly consistent to detect glass but is still affected by the issues such as sensor inaccuracies, sensor reliability, scan mismatching due to glass, sensor model, probabilistic approaches for sensor fusion, sensor registration, etc. The paper aims to discuss these issues.

This paper presents a modified framework – Advanced Laser and Sonar Framework (ALSF) – to fuse the sensory information of a laser scanner and sonar to reduce the uncertainty caused by glass in an environment by selecting the optimal range information corresponding to a selected threshold value. In the proposed approach, the conventional sonar sensor model is also modified to reduce the wrong perception in sonar as an outcome of the diverse range measurement. The laser scan matching algorithm is also modified by taking out the small cluster of laser point (w.r.t. range information) to get efficient perception.

The probability of the occupied cells w.r.t. the modified sonar sensor model becomes consistent corresponding to diverse sonar range measurement. The scan matching technique is also modified to reduce the uncertainty caused by glass and high computational load for the efficient and fast pose estimation of the laser sensor/mobile robot to generate robust mapping. These stated modifications are linked with the proposed ALSF technique to reduce the uncertainty caused by glass, inconsistent probabilities and high load computation during the generation of occupancy grid mapping with MDSF. Various real-world experiments are performed with the implementation of the proposed approach on a mobile robot fitted with laser and sonar, and the obtained results are qualitatively and quantitatively compared with conventional approaches.

The proposed ASIF approach generates efficient perception of the complex environment contains glass and can be implemented for various robotics applications.

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 main purpose of this paper is to investigate two key elements of localization and mapping of Autonomous Underwater Vehicle (AUV), i.e. to overview various sensors and algorithms used for underwater localization and mapping, and to make suggestions for future research.

The authors first review various sensors and algorithms used for AUVs in the terms of basic working principle, characters, their advantages and disadvantages. The statistical analysis is carried out by studying 35 AUV platforms according to the application circumstances of sensors and algorithms.

As real‐world applications have different requirements and specifications, it is necessary to select the most appropriate one by balancing various factors such as accuracy, cost, size, etc. Although highly accurate localization and mapping in an underwater environment is very difficult, more and more accurate and robust navigation solutions will be achieved with the development of both sensors and algorithms.

This paper provides an overview of the state of art underwater localisation and mapping algorithms and systems. No experiments are conducted for verification.

The paper will give readers a clear guideline to find suitable underwater localisation and mapping algorithms and systems for their practical applications in hand.

There is a wide range of audiences who will benefit from reading this comprehensive survey of autonomous localisation and mapping of UAVs.

The paper will provide useful information and suggestions to research students, engineers and scientists who work in the field of autonomous underwater vehicles.

The purpose of this study is to develop a new positioning method for remotely operated vehicle (ROV) in the nuclear power plant. The ROV of the nuclear power plant is developed to inspect the reactor cavity pools, the component pools and spent-fuel storage pools. To enhance the operational safety, the ability of localizing the ROV is indispensable.

Therefore, the positioning method is proposed based on the MEMS inertial measurement unit and mechanical scanning sonar in this paper. Firstly, the ROV model and on board sensors are introduced in detail. Then the sensor-based Kalman filter is deduced for attitude estimation. After that, the positioning method is proposed that divided into static positioning and dynamic positioning. The improved iterative closest point-Kalman filter is deduced to estimate the global position by the whole circle scanning sonar data in static, and the relative positioning method is proposed by the small scale scanning sonar data in dynamic.

The performance of the proposed method is verified by comparing with the visual positioning system. Finally, the effectiveness of the proposed method is proved by the experiment in the reactor simulation pool of the Daya Bay Nuclear Power Plant.

The research content of this manuscript is aimed at the specific application needs of nuclear power plants and has high theoretical significance and application value.

The relation between the output of the fluxset sensor and the magnetic field is established by the numerical and experimental investigation of an ECT set‐up. A fast calculation method has been developed for obtaining the magnetic field generated by the interaction of the probe and the crack in a finite plate by superimposing the results obtained by the analysis of a finite plate without a crack and an infinite plate with a crack. The calculations are made by FEM and boundary integral methods, respectively. The relationship between the measured output and the magnetic field is obtained by calculating the calibration factors giving the best fit of the two sets of data. Based on the results, a numerical tool is developed for the quantitative evaluation of magnetic field sensors applied to the measurement of spatially inhomogeneous fields.

Aims to make a mobile robot able to build accurate 2D and 3D models of its environment while navigating autonomously.

2D map building is performed using a laser range scanner. The map is used by the robot to both localize itself and recognize places already explored. This is the well‐known simultaneous localization and mapping (SLAM) problem. 3D model reconstruction, instead, uses computer vision techniques based on feature extraction and matching.

The experimental results illustrate the validity and accuracy of the reconstructed maps of the environment and enable the robot to navigate autonomously in indoor environments, such as museums, hospitals, airports, offices and so on. Such a robot can play a major role in different tasks such as surveillance, image‐based rendering, remote fruition of hardly accessible sites, monitoring and maintenance applications, reverse engineering in construction. In these areas accurate 3D models in addition to 2D maps can convey a lot of very useful information.

The main contribution of the paper is an interesting integration of different algorithms in an experimental platform that performs 2D map building using a laser range scanner, autonomous navigation and 3D reconstruction of the areas of particular interest.