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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.

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 paper is to describe a novel application of the recently introduced concept from computer vision to self‐localization of a walking robot in unstructured environments. The technique described in this paper enables a walking robot with a monocular vision system (single camera) to obtain precise estimates of its pose with regard to the six degrees of freedom. This capability is essential in search and rescue missions in collapsed buildings, polluted industrial plants, etc.

The Parallel Tracking and Mapping (PTAM) algorithm and the Inertial Measurement Unit (IMU) are used to determine the 6‐d.o.f. pose of a walking robot. Bundle‐adjustment‐based tracking and structure reconstruction are applied to obtain precise camera poses from the monocular vision data. The inclination of the robot's platform is determined by using IMU. The self‐localization system is used together with the RRT‐based motion planner, which allows to walk autonomously on rough, previously unknown terrain. The presented system operates on‐line on the real hexapod robot. Efficiency and precision of the proposed solution are demonstrated by experimental data.

The PTAM‐based self‐localization system enables the robot to walk autonomously on rough terrain. The software operates on‐line and can be implemented on the robot's on‐board PC. Results of the experiments show that the position error is small enough to allow robust elevation mapping using the laser scanner. In spite of the unavoidable feet slippages, the walking robot which uses PTAM for self‐localization can precisely estimate its position and successfully recover from motion execution errors.

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

Precise self‐localization may be one of the most important factors enabling the use of walking robots in practical USAR missions. The results of research on precise self‐localization in 6‐d.o.f. may be also useful for autonomous robots in other application areas: construction, agriculture, military.

The vision‐based self‐localization algorithm used in the presented research is not new, but the contribution lies in its implementation/integration on a walking robot, and experimental evaluation in the demanding problem of precise self‐localization in rough terrain.

The authors present a low-cost unmanned aerial vehicle (UAV) for autonomous flight and navigation in GPS-denied environments using an off-the-shelf smartphone as its core on-board processing unit. Thereby, the approach is independent from additional ground hardware and the UAV core unit can be easily replaced with more powerful hardware that simplifies setup updates as well as maintenance. The paper aims to discuss these issues.

The UAV is able to map, locate and navigate in an unknown indoor environment fusing vision-based tracking with inertial and attitude measurements. The authors choose an algorithmic approach for mapping and localization that does not require GPS coverage of the target area; therefore autonomous indoor navigation is made possible.

The authors demonstrate the UAVs capabilities of mapping, localization and navigation in an unknown 2D marker environment. The promising results enable future research on 3D self-localization and dense mapping using mobile hardware as the only on-board processing unit.

The proposed autonomous flight processing pipeline robustly tracks and maps planar markers that need to be distributed throughout the tracking volume.

Due to the cost-effective platform and the flexibility of the software architecture, the approach can play an important role in areas with poor infrastructure (e.g. developing countries) to autonomously perform tasks for search and rescue, inspection and measurements.

The authors provide a low-cost off-the-shelf flight platform that only requires a commercially available mobile device as core processing unit for autonomous flight in GPS-denied areas.

This paper aims to study the localization problem for autonomous industrial vehicles in the complex industrial environments. Aiming for practical applications, the pursuit is to build a map-less localization system which can be used in the presence of dynamic obstacles, short-term and long-term environment changes.

The proposed system contains four main modules, including long-term place graph updating, global localization and re-localization, location tracking and pose registration. The first two modules fully exploit the deep-learning based three-dimensional point cloud learning techniques to achieve the map-less global localization task in large-scale environment. The location tracking module implements the particle filter framework with a newly designed perception model to track the vehicle location during movements. Finally, the pose registration module uses visual information to exclude the influence of dynamic obstacles and short-term changes and further introduces point cloud registration network to estimate the accurate vehicle pose.

Comprehensive experiments in real industrial environments demonstrate the effectiveness, robustness and practical applicability of the map-less localization approach.

This paper provides comprehensive experiments in real industrial environments.

The system can be used in the practical automated industrial vehicles for long-term localization tasks. The dynamic objects, short-/long-term environment changes and hardware limitations of industrial vehicles are all considered in the system design. Thus, this work moves a big step toward achieving real implementations of the autonomous localization in practical industrial scenarios.

One difficult problem in robotics is localization: the ability of a mobile robot to determine its position in the environment. Roboticists around the globe have been working to find a solution to localization for more than 20 years; however, only in the past 4‐5 years we have seen some promising results. In this work, we describe a first‐of‐a‐kind, breakthrough technology for localization that requires only one low‐cost camera (less than 50USD) and odometry to provide localization. Because of its low‐cost and robust performance in realistic environments, this technology is particularly well‐suited for use in consumer and commercial applications.

This study aims to achieve superior localization and mapping performance in point cloud degradation scenarios through the effective removal of dynamic obstacles. With the continuous development of various technologies for autonomous vehicles, the LIDAR-based Simultaneous localization and mapping (SLAM) system is becoming increasingly important. However, in SLAM systems, effectively addressing the challenges of point cloud degradation scenarios is essential for accurate localization and mapping, with dynamic obstacle removal being a key component.

This paper proposes a method that combines adaptive feature extraction and loop closure detection algorithms to address this challenge. In the SLAM system, the ground point cloud and non-ground point cloud are separated to reduce the impact of noise. And based on the cylindrical projection image of the point cloud, the intensity features are adaptively extracted, the degradation direction is determined by the degradation factor and the intensity features are matched with the map to correct the degraded pose. Moreover, through the difference in raster distribution of the point clouds before and after two frames in the loop process, the dynamic point clouds are identified and removed, and the map is updated.

Experimental results show that the method has good performance. The absolute displacement accuracy of the laser odometer is improved by 27.1%, the relative displacement accuracy is improved by 33.5% and the relative angle accuracy is improved by 23.8% after using the adaptive intensity feature extraction method. The position error is reduced by 30% after removing the dynamic target.

Compared with LiDAR odometry and mapping algorithm, the method has greater robustness and accuracy in mapping and localization.

The purpose of this paper is to propose a localizability-based particle filtering localization algorithm for mobile robots to maintain localization accuracy in the high-occluded and dynamic environments with moving people.

First, the localizability of mobile robots is defined to evaluate the influences of both the dynamic obstacles and prior-map on localization. Second, based on the classical two-sensor track fusion algorithm, the odometer-based proposal distribution function (PDF) is corrected, taking account of the localizability. Then, the corrected PDF is introduced into the classical PF with “roulette” re-sampling. Finally, the robot pose is estimated according to all the particles.

The experimental results show that, first, it is necessary to consider the influence of the prior-map during the localization in the high-occluded and dynamic environments. Second, the proposed algorithm can maintain an accurate and robust robot pose in the high-occluded and dynamic environments. Third, its real timing is acceptable.

When the odometer error and occlusion caused by the dynamic obstacles are both serious, the proposed algorithm also has a probability evolving into the kidnap problem. But fortunately, such serious situations are not common in practice.

To check the ability of real application, we have implemented the proposed algorithm in the campus cafeteria and metro station using an intelligent wheelchair. To better help the elderly and disabled people during their daily lives, the proposed algorithm will be tested in a social welfare home in the future.

The localizability of mobile robots is defined to evaluate the influences of both the dynamic obstacles and prior-map on localization. Based on the localizability, the odometer-based PDF is corrected properly.

Autonomous mobile robots (AMRs) play a crucial role in industrial and service fields. The paper aims to build a LiDAR-based simultaneous localization and mapping (SLAM) system used by AMRs to overcome challenges in dynamic and changing environments.

This research introduces SLAM-RAMU, a lifelong SLAM system that addresses these challenges by providing precise and consistent relocalization and autonomous map updating (RAMU). During the mapping process, local odometry is obtained using iterative error state Kalman filtering, while back-end loop detection and global pose graph optimization are used for accurate trajectory correction. In addition, a fast point cloud segmentation module is incorporated to robustly distinguish between floor, walls and roof in the environment. The segmented point clouds are then used to generate a 2.5D grid map, with particular emphasis on floor detection to filter the prior map and eliminate dynamic artifacts. In the positioning process, an initial pose alignment method is designed, which combines 2D branch-and-bound search with 3D iterative closest point registration. This method ensures high accuracy even in scenes with similar characteristics. Subsequently, scan-to-map registration is performed using the segmented point cloud on the prior map. The system also includes a map updating module that takes into account historical point cloud segmentation results. It selectively incorporates or excludes new point cloud data to ensure consistent reflection of the real environment in the map.

The performance of the SLAM-RAMU system was evaluated in real-world environments and compared against state-of-the-art (SOTA) methods. The results demonstrate that SLAM-RAMU achieves higher mapping quality and relocalization accuracy and exhibits robustness against dynamic obstacles and environmental changes.

Compared to other SOTA methods in simulation and real environments, SLAM-RAMU showed higher mapping quality, faster initial aligning speed and higher repeated localization accuracy.

In recent decades, the field of robotic mapping has witnessed widespread research and development in light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) techniques. This paper aims to provide a significant reference for researchers and engineers in robotic mapping.

This paper focused on the research state of LiDAR-based SLAM for robotic mapping as well as a literature survey from the perspective of various LiDAR types and configurations.

This paper conducted a comprehensive literature review of the LiDAR-based SLAM system based on three distinct LiDAR forms and configurations. The authors concluded that multi-robot collaborative mapping and multi-source fusion SLAM systems based on 3D LiDAR with deep learning will be new trends in the future.

To the best of the authors’ knowledge, this is the first thorough survey of robotic mapping from the perspective of various LiDAR types and configurations. It can serve as a theoretical and practical guide for the advancement of academic and industrial robot mapping.