A novel approach for the global localization problem

Life-long and robust operation are important challenges to be solved towards everyday usability of service robots. Global localization is of particular interest for real-world applications. If a robot would not be able to relocalize itself within a known map, all positions stored by the robot (rooms, objects, etc.) would become obsolete. Although Simultaneous Localization and Mapping (SLAM) allows to initially map new and unknown environments and to keep track of environmental changes, it does not solve the global localization problem. Each time SLAM is restarted at different locations, it introduces a new map and a new frame of reference. In this paper, we propose a solution to the global localization problem which uses a SLAM generated feature map. The approach is demonstrated with an omnicam and bearing-only features. A new way to weight hypotheses and to sort out false hypotheses results in fast convergence even with arbitrary relocalization paths. The combined approach is a further step towards life-long operation of service robots and covers every part of a robot lifecycle, ranging from a setup via SLAM to efficient global localization for reuse of maps and object poses after restart.

The importance of indoor mapping has surged across various applications, encompassing human navigation, indoor exploration, and hazardous site mapping. With satellite positioning systems ineffective indoors, alternative navigational sensors and methods, such as inertial measurement units (IMU) and simultaneous localization and mapping algorithms (SLAM), have been harnessed to furnish precise indoor positioning for mapping endeavors. Leveraging LiDAR-based SLAM, which is sensitive to environmental textures and various pose configurations, addresses these challenges. In order to accomplish reliable and precise indoor 3D mapping, this study combines the Velodyne VLP-16 3D-LiDAR sensor with the Pixhawk Cube Orange, which has a 9 Degrees of Freedom (DOF) IMU sensor. The study uses the LIO-SAM (LiDAR Inertial Odometry and Simultaneous Localization and Mapping) technique, which integrates data from LiDAR and IMU to increase mapping precision. It is carried out at the BAAK Dr. Angka Building, Institut Teknologi Sepuluh Nopember, Surabaya. The experimental results reveal that the 3D indoor mapping exhibits commendable visualization and high geometric accuracy, meeting the LoD 3 criteria. Validation using a roll meter yielded a root mean square error (RMSE) of 0.3552 meters, indicating considerable accuracy. The accuracy was further validated by showing LiDAR point cloud errors ranging from 7.92% to 15.75%. Linear regression analysis confirmed a high correlation (R2 0.9954) between LiDAR point cloud measurements and manual measurements, substantiating the method’s reliability. In order to accomplish reliable and precise indoor 3D mapping, this study combines the Velodyne VLP-16 3D-LiDAR sensor with the Pixhawk Cube Orange, which has a 9 Degrees of Freedom (DOF) IMU sensor. The study uses the LIO-SAM (LiDAR Inertial Odometry and Simultaneous Localization and Mapping) technique, which integrates data from LiDAR and IMU to increase mapping precision. It is carried out at the BAAK Dr. Angka Building, Institut Teknologi Sepuluh Nopember, Surabaya.

Augmented reality (AR) can support neurosurgical procedures by integrating 3-dimensional (3D) images into real-world views to help anatomical understanding. However, current systems are cumbersome and not attractive for trainees. Studies on wearable AR devices show promise but need further refinement. This preliminary report assesses a novel augmented reality navigation system (ARnavS) using a simultaneous localization and mapping algorithm provided with the Vuforia SDK and a quick response code. The system was applied to burr-hole surgeries in 10 cases of chronic subdural hematoma to evaluate its convenience and accuracy. Ten patients with 11 hematomas underwent burr-hole surgeries using ARnavS. The system was effective without equipment issues, with an average registration time of 2.6 minutes. Discrepancy between planned and actual burr-hole locations was 3.5 mm. Skin incisions avoided 3D image of the superficial temporal artery, with no superficial temporal artery damage in any case. The mean surgical time was 37.1 minutes, and no severe postoperative bleeding occurred. Our ARnavS for burr-hole surgery in chronic subdural hematoma offers good 3D imaging with quick registration. It provided surgeons with better anatomical comprehension, helping to avoid injury to critical structures. Unlike conventional systems, ARnavS allows direct visualization of 3D graphics in the surgical field, beneficial for less-experienced neurosurgeons and medical students. While further studies are needed, the AR navigation system with simultaneous localization and mapping algorithm and quick response code showed precise intraoperative 3D imaging with sufficient accuracy after quick registration. While further studies are needed, it has demonstrated practical usefulness in clinical neurosurgical procedures.

When mobile robots perform their missions, the self-localization is needed basically. Several past researches established how to obtain their location information from the environment by using a distance sensor or a camera. However, these methods have map-making problem when the environment changes and localization problem while the robot moves from sensing features has typical affine and occlusion characteristics. This paper presents a localizer for mobile robot that travels around indoor environments. Our module uses the only one sensor, a single camera looking up the ceiling. There is no efficient enough SLAM* (Simultaneous Localization And Mapping) algorithm working on embedded system. The initial difficulty of vision based SLAM is computational complexity to acquire reliable feature on their algorithm. To reduce the computational complexity, we use the ceiling segmentation to extract line features of ceiling area. Line features are extracted from the boundaries between the ceiling and walls. The line features have advantages over point features for its robustness to environmental variation and structural information helpful to data association. Extended Kalman Filter is used to estimate the pose of a robot and build the ceiling map with line features. The experiment is practiced in our indoor test bed and the proposed algorithm is proved by the experimental results.*SLAM: Simultaneous localization and mapping is a technique used by robots and autonomous vehicles to build up a map within an unknown environment or to update a map within a known environment while at the same time keeping track of their current location.

In this paper we describe a scalable algorithm for the simultaneous mapping and localization (SLAM) problem. SLAM is the problem of acquiring a map of a static environment with a mobile robot. The vast majority of SLAM algorithms are based on the extended Kalman filter (EKF). In this paper we advocate an algorithm that relies on the dual of the EKF, the extended information filter (EIF). We show that when represented in the information form, map posteriors are dominated by a small number of links that tie together nearby features in the map. This insight is developed into a sparse variant of the EIF, called the sparse extended information filter (SEIF). SEIFs represent maps by graphical networks of features that are locally interconnected, where links represent relative information between pairs of nearby features, as well as information about the robot’s pose relative to the map. We show that all essential update equations in SEIFs can be executed in constant time, irrespective of the size of the map. We also provide empirical results obtained for a benchmark data set collected in an outdoor environment, and using a multi-robot mapping simulation.

When solving the problem of simultaneous localization and mapping (SLAM), a standard extended Kalman filter (EKF) is subject to linearization errors and causes optimistic estimation. This paper proposes a submap algorithm, which builds a weighted least squares (WLS) constraint between two adjacent submaps according to the different estimations of the common features and the relationship between the vehicle poses in the corresponding submaps. By establishing the constraint equation after loop closing, re-linearization is implemented and each submap’s reference frame tends to its equilibrium position quickly. Experimental results demonstrate that the algorithm could get a globally consistent map and linearization errors are limited in local regions.

Water, a valuable resource, is usually distributed through urban environments by buried pipes. These pipes are difficult to access for inspection, maintenance and repair. This makes in-pipe robots an appealing technology for inspecting water pipes and localising damage prior to repair from above ground. Accurate localisation of damage is of critical importance because of the costs associated with excavating roads, disrupting traffic and disrupting the water supply. The problem is that pipes tend to be relatively featureless making robot localisation a challenging problem. In this paper we propose a novel simultaneous localisation and mapping (SLAM) algorithm for metal water pipes. The approach we take is to excite pipe vibration with a hydrophone (sound induced vibration), which leads to a map of pipe vibration amplitude over space. We then develop a SLAM algorithm that makes use of this new type of map, where the estimation method is based on the Rao-Blackwellised particle filter (RBPF), termed PipeSLAM. The approach is also suited to SLAM in plastic water pipes using a similar type of map derived from ultrasonic sensing. We successfully demonstrate the feasibility of the approach using a combination of experimental and simulation data.

The tethered mobile robot can be used to detect extreme terrain. During the movement, it will inevitably contact or even entangle with obstacles. In this paper, a simultaneous localization and mapping (SLAM) algorithm for tethered robots based on the UFastSLAM framework is proposed, which uses particle filtering and unscented transformation to estimate the pose of the tethered robot and the positions of the anchor points. The simulation results show that the algorithm can effectively estimate the position of anchor points and improve the performance of robot pose estimation simultaneously.

Simultaneous localization and mapping (SLAM) is a central and complex problem in robot research community. In SLAM, extended Kalman filter (EKF) implementation is widely used to localize the robot and build the environment map incrementally. In this paper, we propose a strong tracking filter (STF) SLAM algorithm. This algorithm applies STF to deal with the non-linear estimated problem in SLAM instead of EKF. It can make the performance of the nonlinear filter approximate to that of optimal linear Kalman Filter (KF), so it can construct high accuracy maps and locate the robot more accurately than EKF SLAM. Simulation experiments illustrate the superior performance of our approach compared to EKF SLAM algorithm.

In this work, a 2D vision-based simultaneous localization and mapping algorithm is developed for an unmanned aerial vehicle flying at a constant altitude. We use visual features of images obtained from an on-board camera to identify different landmarks. Using these landmarks we apply the well-known extended Kalman filter to the SLAM problem and present some simulation results.

Autonomous mobile machines use onboard sensors for navigation and obstacle avoidance. The accuracy of the sensor data in global frame is however dependent on the localization accuracy of the machine. Simultaneous localization and mapping algorithms (SLAM) are widely used with 3D laser scanners for mapping the world. They use scan matching algorithms to solve the accuracy problem by matching prior sensor data of the environment with the newly acquired data. However matching scans is not always possible. Insufficient amount of prior data or too few features in the scan can prevent the scan matching algorithm from finding a match. Thus it is important that also the mapping algorithm is tolerant to some degree of error in localization and calibration. We present a method for generating obstacle maps from smaller data segments at a time, thus making the mapping system more tolerant to navigation and calibration errors. The obstacle mapping method is tested with modified Avant multipurpose loader.

This paper introduces EIF-SlideWindow, a novel enhancement of the Extended Information Filter (EIF) algorithm for Simultaneous Localization and Mapping (SLAM). Traditional EIF-SLAM, while effective in many scenarios, struggles with inaccuracies in highly non-linear systems or environments characterized by significant non-Gaussian noise. Moreover, the computational complexity of EIF/EKF-SLAM scales with the size of the environment, often resulting in performance bottlenecks. Our proposed EIF-SlideWindow approach addresses these limitations by maintaining a fixed-size information matrix and vector, ensuring constant-time processing per robot step, regardless of trajectory length. This is achieved through a sliding window mechanism centered on the robot’s pose, where older landmarks are systematically replaced by newer ones. We assess the effectiveness of EIF-SlideWindow using simulated data and demonstrate that it outperforms standard EIF/EKF-SLAM in both accuracy and efficiency. Additionally, our implementation leverages PyTorch for matrix operations, enabling efficient execution on both CPU and GPU. Additionally, the code for this approach is made available for further exploration and development.

Feature point extraction and matching are the key modules in the front-end of visual simultaneous localization and mapping (SLAM), which determines the accuracy of locating and map Nbuilding robots. To obtain a good performance, the image extraction algorithm being applied should have high efficiency and robustness to the changes of the surrounding environment. In this paper, three widely used feature extraction algorithms, namely Scale-invariant feature transform (SIFT), speed up robust features (SURF) and oriented FAST, and rotated BRIEF (ORB), are studied. The basic concepts and details of the three algorithms are introduced. Then the execution time of the three algorithms is compared to evaluate their efficiency. Meanwhile, the key points matching rate of the three algorithms are compared under various transformations and distortions. The experiment results show that ORB has higher efficiency than SIFT and SURF. However, SIFT has better robustness to image rotation, illumination change, scaling transformation, Gaussian noise and salt-and-pepper noise. In addition, the performance of the ORB-SLAM system is tested on different datasets. It has great ability in camera trajectory estimation and map building and therefore is suitable for deploying in industry.

Simultaneous localization and mapping (SLAM) is a key technology for mobile robots operating under unknown environment. While FastSLAM algorithm is a popular solution to the SLAM problem, it suffers from two major drawbacks: one is particle set degeneracy due to lack of observation information in proposal distribution design of the particle filter; the other is errors accumulation caused by linearization of the nonlinear robot motion model and the nonlinear environment observation model. For the purpose of overcoming the above problems, a new iterated sigma point FastSLAM (ISP-FastSLAM) algorithm is proposed. The main contribution of the algorithm lies in the utilization of iterated sigma point Kalman filter (ISPKF), which minimizes statistical linearization error through Gaussian-Newton iteration, to design an optimal proposal distribution of the particle filter and to estimate the environment landmarks. On the basis of Rao-Blackwellized particle filter, the proposed ISP-FastSLAM algorithm is comprised by two main parts: in the first part, an iterated sigma point particle filter (ISPPF) to localize the robot is proposed, in which the proposal distribution is accurately estimated by the ISPKF; in the second part, a set of ISPKFs is used to estimate the environment landmarks. The simulation test of the proposed ISP-FastSLAM algorithm compared with FastSLAM2.0 algorithm and Unscented FastSLAM algorithm is carried out, and the performances of the three algorithms are compared. The simulation and comparing results show that the proposed ISP-FastSLAM outperforms other two algorithms both in accuracy and in robustness. The proposed algorithm provides reference for the optimization research of FastSLAM algorithm.

State of the art approaches to Multi-robot localization and mapping still present multiple issues to be improved, offering a wide range of possibilities for researchers and technology. This paper presents a new algorithm for visual Multi-robot simultaneous localization and mapping, used to join, in a common reference system, several trajectories of different robots that participate simultaneously in a common mission. One of the main problems in centralized configurations, where the leader can receive multiple data from the rest of robots, is the limited communications bandwidth that delays the data transmission and can be overloaded quickly, restricting the reactive actions. This paper presents a new approach to Multi-robot visual graph Simultaneous Localization and Mapping (SLAM) that aims to perform a joined topological map, which evolves in different directions according to the different trajectories of the different robots. The main contributions of this new strategy are centered on: (a) reducing to hashes of small dimensions the visual data to be exchanged among all agents, diminishing, in consequence, the data delivery time, (b) running two different phases of SLAM, intra- and inter-session, with their respective loop-closing tasks, with a trajectory joining action in between, with high flexibility in their combination, (c) simplifying the complete SLAM process, in concept and implementation, and addressing it to correct the trajectory of several robots, initially and continuously estimated by means of a visual odometer, and (d) executing the process online, in order to assure a successful accomplishment of the mission, with the planned trajectories and at the planned points. Primary results included in this paper show a promising performance of the algorithm in visual datasets obtained in different points on the coast of the Balearic Islands, either by divers or by an Autonomous Underwater Vehicle (AUV) equipped with cameras.