A Low-Cost MMS Approach to the Simultaneous Localization and Mapping Problem

The mobile robot which accomplishes a work in explored region does not know location information of surroundings. Traditionally, simultaneous localization and mapping(SLAM) algorithms solve the localization and mapping problem in explored regions. Among the several SLAM algorithms, the EKF (Extended Kalman Filter) based SLAM is the scheme most widely used. The EKF is the optimal sensor fusion method which has been used for a long time. The odometric error caused by an encoder can be compensated by an EKF, which fuses different types of sensor data with weights proportional to the uncertainty of each sensor. In many cases the EKF based SLAM requires artificially installed features, which causes difficulty in actual implementation. Moreover, the computational complexity involved in an EKF increases as the number of features increases. And SLAM is a weak point of long operation time. Therefore, this paper presents a symmetrical model based SLAM algorithm (called M-SLAM).

Mobile robot Simultaneous Localization and Mapping (SLAM) problem is one of the most active research areas in robotics. In the research and simulation of SLAM, MATLAB-based simulators are widely used due to their comprehensive functionalities and simple usage. In this paper, the main open source MATLAB-based simulators for SLAM and their properties are listed. Two representative ones are concretely introduced from the aspects of data creation and import, motion model and observation model, and algorithms implementation. Simulation results of these two simulators indicate that MATLAB-based simulators are convenient and helpful in the robot SLAM research when developing new algorithms and when comparing accuracy, consistency or convergence of different algorithms. The SLAM algorithms widely used in MATLAB-based simulators, including Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) based SLAM algorithm and FastSLAM algorithm, are also introduced.

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.

As the processing power and the science of robotics advances, robots are becoming more and more available to the masses and convenient to use. One instance of the applications of robots are the moving robots. These robots essentially move and explore the world they are located in, mainly: indoor robots. In our laboratory we are currently working on an indoor robot which serves the purpose of teleperesence. One of the challenges in building such robot is the navigation, mainly planning routes, and avoiding the obstacles that are in front of it. In order to navigate, the robot needs to have a map of the environment where it is located. Plus, it needs to locate itself in that map. This paper discusses an improved obstacle avoidance algorithm to our laboratory's experimental telepresence robot. The improved algorithm is based on Simultaneous Localization and Mapping (SLAM) This new obstacle avoidance algorithm uses a Kinect 2 to collect depth frames and RGB images then synthesize them into Point Cloud files (PCL). These Point clouds are processed and gathered into a 3D map and used as input to the Simultaneous Localization and Mapping (SLAM) algorithm to help the robot navigate as smooth as possible in its indoor environment. The robot navigates by creating a map of its surroundings and constantly localizing itself in that map.

In recent years, low-cost and lightweight RGB and depth (RGB-D) sensors, such as Microsoft Kinect, have made available rich image and depth data, making them very popular in the field of simultaneous localization and mapping (SLAM), which has been increasingly used in robotics, self-driving vehicles, and augmented reality. The RGB-D SLAM constructs 3D environmental models of natural landscapes while simultaneously estimating camera poses. However, in highly variable illumination and motion blur environments, long-distance tracking can result in large cumulative errors and scale shifts. To address this problem in actual applications, in this study, we propose a novel multithreaded RGB-D SLAM framework that incorporates a highly accurate prior terrestrial Light Detection and Ranging (LiDAR) point cloud, which can mitigate cumulative errors and improve the system’s robustness in large-scale and challenging scenarios. First, we employed deep learning to achieve system automatic initialization and motion recovery when tracking is lost. Next, we used terrestrial LiDAR point cloud to obtain prior data of the landscape, and then we applied the point-to-surface inductively coupled plasma (ICP) iterative algorithm to realize accurate camera pose control from the previously obtained LiDAR point cloud data, and finally expanded its control range in the local map construction. Furthermore, an innovative double window segment-based map optimization method is proposed to ensure consistency, better real-time performance, and high accuracy of map construction. The proposed method was tested for long-distance tracking and closed-loop in two different large indoor scenarios. The experimental results indicated that the standard deviation of the 3D map construction is 10 cm in a mapping distance of 100 m, compared with the LiDAR ground truth. Further, the relative cumulative error of the camera in closed-loop experiments is 0.09%, which is twice less than that of the typical SLAM algorithm (3.4%). Therefore, the proposed method was demonstrated to be more robust than the ORB-SLAM2 algorithm in complex indoor environments.

Simultaneous localization and mapping (SLAM) techniques are widely researched, since they allow the simultaneous creation of a map and the sensors’ pose estimation in an unknown environment. Visual-based SLAM techniques play a significant role in this field, as they are based on a low-cost and small sensor system, which guarantees those advantages compared to other sensor-based SLAM techniques. The literature presents different approaches and methods to implement visual-based SLAM systems. Among this variety of publications, a beginner in this domain may find problems with identifying and analyzing the main algorithms and selecting the most appropriate one according to his or her project constraints. Therefore, we present the three main visual-based SLAM approaches (visual-only, visual-inertial, and RGB-D SLAM), providing a review of the main algorithms of each approach through diagrams and flowcharts, and highlighting the main advantages and disadvantages of each technique. Furthermore, we propose six criteria that ease the SLAM algorithm’s analysis and consider both the software and hardware levels. In addition, we present some major issues and future directions on visual-SLAM field, and provide a general overview of some of the existing benchmark datasets. This work aims to be the first step for those initiating a SLAM project to have a good perspective of SLAM techniques’ main elements and characteristics.

This paper describes an approach that combines the navigation data given by a Doppler Velocity Logs (DVL), the MTi Motion Reference Unit (MRU) and a Mechanically Scanned Imaging Sonar (MSIS) as a principal sensor to efficiently solve underwater Simultaneous Localization and Mapping (SLAM) problem in structured environments such as marine platforms, harbors, or dams, etc. The MSIS has been chosen of its capacity to produce a rich representation of the environment. In recent years, to solve the SLAM Autonomous Underwater Vehicle (AUV) problem, very few solutions have been proposed. Our contribution has introduced a method based on the Nonlinear H-infinity filter \((NH\infty )\) to solve the SLAM-AUV problem. In this work, the \(NH\infty \)-SLAM algorithm is implemented to construct a map in partially structured environments and localize the AUV within this map. The data-set used in this paper are taken from the experiments carried out in a marina located in the Costa Brava (Spain) with the Ictineu AUV which is necessary to test different SLAM algorithms. The validation of the proposed algorithm through simulation in offline is presented and compared to the EKF-SLAM algorithm. The \(NH\infty \)-SLAM algorithm provides an accurate estimate than EKF-SLAM and good results were obtained.

Owing to low cost and relatively accurate range measurement, ultrasonic sensors are widely used in various simultaneous localisation and mapping (SLAM) applications. In spite of the abundance of ultrasonic sensor based SLAM applications, a simple and efficient algorithm for an ultrasonic sensor based positioning system with good accuracy and low computational complexity has not yet emerged. The major difficulty is the trade-off between localisation accuracy and computational complexity in most SLAM algorithms, such as extended Kalman filter (EKF) and particle filter. Typically, they improve localisation accuracy by increasing the density of the landmarks, as a result leading to high computational complexity of algorithms and limiting the utilisation of algorithms into online SLAM systems. This study addresses an improved particle filter algorithm to solve ultrasonic sensor based 2D range-only SLAM problem with relatively good accuracy and low computational complexity. This algorithm uses a simple four fixed features based system model to limit the density of the landmarks. A technique called map adjustment is proposed to increase the accuracy and efficiency of the algorithm. Using map adjustment, the proposed particle filter algorithm can improve localisation accuracy and lower computational complexity. The experiment results demonstrate that this algorithm has a better performance than conventional particle filter localisation algorithm.

The accuracy and stability of front-end point cloud registration algorithms are crucial for the mapping and localization precision in laser SLAM (simultaneous localization and mapping) systems. Traditional point-to-line and point-to-plane iterative closest point (ICP) registration algorithms, widely used in SLAM front ends, often suffer from low efficiency, significant data dependency during the matching process, and a propensity for local optima. This registration method exhibits a more pronounced local optimum issue in large-scale SLAM mapping, thereby diminishing matching accuracy and increasing reliance on initial values. To address these limitations, this paper introduces NI-LIO, a novel SLAM algorithm that integrates ICP with normal distributions transform (NDT) to enhance localization accuracy, computational efficiency and robustness. By combining the precision of ICP with the robustness of NDT, the proposed algorithm significantly improves system stability and localization accuracy. The analysis of mapping and localization experiments indicates a significant reduction in errors compared to traditional SLAM algorithms, with experiments showing a REMS value decrease of over 20%. Compared to ALOAM, FAST_LIO2 and Lego-LOAM algorithms, the new NI-LIO algorithm shows improvements in both accuracy and stability, enabling the construction of a more precise and consistent global map. This algorithm exhibits excellent adaptability to various environments.

Visual simultaneous localization and mapping (SLAM) is a critical technology for autonomous navigation in dynamic environments. However, traditional SLAM algorithms often struggle to maintain accuracy in highly dynamic environments, where elements undergo significant, rapid, and unpredictable changes, leading to asymmetric information acquisition. Aiming to improve the accuracy of the SLAM algorithm in a dynamic environment, a dynamic SLAM algorithm based on deep learning is proposed. Firstly, YOLOv10n is used to improve the front end of the system, and semantic information is added to each frame of the image. Then, ORB-SLAM2 is used to extract feature points in each region of each frame and retrieve semantic information from YOLOv10n. Finally, through the map construction thread, the dynamic object feature points extracted by YOLOv10n are eliminated, and the construction of a static map is realized. The experimental results show that the accuracy of the proposed algorithm is improved by more than 96% compared with the state-of-the-art ORB-SLAM2 in a highly dynamic environment. Compared with other dynamic SLAM algorithms, the proposed algorithm has improved both accuracy and runtime.

Simultaneous Localization and Mapping (SLAM) is one of the most challenging research areas within computer and machine vision for automated scene commentary and explanation. The SLAM technique has been a developing research area in the robotics context during recent years. By utilizing the SLAM method robot can estimate the different positions of the robot at the distinct points of time which can indicate the trajectory of robot as well as generate a map of the environment. SLAM has unique traits which are estimating the location of robot and building a map in the various types of environment. SLAM is effective in different types of environment such as indoor, outdoor district, Air, Underwater, Underground and Space. Several approaches have been investigated to use SLAM technique in distinct environments. The purpose of this paper is to provide an accurate perceptive review of case history of SLAM relied on laser/ultrasonic sensors and camera as perception input data. In addition, we mainly focus on three paradigms of SLAM problem with all its pros and cons. In the future, use intelligent methods and some new idea will be used on visual SLAM to estimate the motion intelligent underwater robot and building a feature map of marine environment.

LiDAR-based simultaneous localization and mapping (SLAM) approaches have obtained considerable success in autonomous robotic systems. This is in part owing to the high accuracy of robust SLAM algorithms and the emergence of new and lower-cost LiDAR products. This study benchmarks the current state-of-the-art LiDAR SLAM algorithms with a multi-modal LiDAR sensor setup, showcasing diverse scanning modalities (spinning and solid state) and sensing technologies, and LiDAR cameras, mounted on a mobile sensing and computing platform. We extend our previous multi-modal multi-LiDAR dataset with additional sequences and new sources of ground truth data. Specifically, we propose a new multi-modal multi-LiDAR SLAM-assisted and ICP-based sensor fusion method for generating ground truth maps. With these maps, we then match real-time point cloud data using a normal distributions transform (NDT) method to obtain the ground truth with a full six-degrees-of-freedom (DOF) pose estimation. These novel ground truth data leverage high-resolution spinning and solid-state LiDARs. We also include new open road sequences with GNSS-RTK data and additional indoor sequences with motion capture (MOCAP) ground truth, complementing the previous forest sequences with MOCAP data. We perform an analysis of the positioning accuracy achieved, comprising ten unique configurations generated by pairing five distinct LiDAR sensors with five SLAM algorithms, to critically compare and assess their respective performance characteristics. We also report the resource utilization in four different computational platforms and a total of five settings (Intel and Jetson ARM CPUs). Our experimental results show that the current state-of-the-art LiDAR SLAM algorithms perform very differently for different types of sensors. More results, code, and the dataset can be found at GitHub.

Right invariant extended Kalman filter (RIEKF) based simultaneous localization and mapping (SLAM) proposed recently has shown to be able to produce more consistent SLAM estimates as compared with traditional EKF based SLAM methods, including some improved EKF SLAM methods such as observability constrained-EKF (OC-EKF) SLAM. Latest results have demonstrated that its performance is very close to optimization based SLAM algorithms such as iSAM. In this paper, we propose to use RIEKF SLAM algorithm in active SLAM where both the predicted SLAM results for choosing control actions and the actual estimated SLAM results applying the selected control actions are computed using RIEKF algorithms. The advantages over traditional EKF based active SLAM are the more accurate and consistent predicted uncertainty estimates which result in robustness of the active SLAM algorithm. The advantages over optimization based active SLAM is the reduced computational cost. Simulation results are presented to validate the advantages of the proposed algorithm³.

Several works have focused on Simultaneous Localization and Mapping (SLAM), which is a topic that has been studied for more than a decade to meet the needs of robots to navigate in an unknown environment. SLAM is an essential perception functionality in several applications, especially in robotics and autonomous vehicles. RGB-D cameras are among the sensors commonly used with recent SLAM algorithms. They provide an RGB image and the associated depth map, making it possible to solve scale drift with less complexity and create a dense 3D environment representation. Many RGB-D SLAM algorithms have been studied and evaluated on publicly available datasets without considering sensor specifications or image acquisition modes that could improve or decrease localization accuracy. In this work, we deal with indoor localization, taking into account the sensor specifications. In this context, our contribution is a deep experimental study to highlight the impact of the sensor acquisition modes on the localization accuracy, and a parametric optimization protocol for a precise localization in a given environment. Furthermore, we apply the proposed protocol to optimize a depth-related parameter of the SLAM algorithm. The study is based on a publicly available dataset in an indoor environment with a depth sensor. The reconstruction results’ analysis is founded on the study of different metrics involving translational and rotational errors. These metrics errors are compared with those obtained with a state-of-the-art stereo vision-based SLAM algorithm.

This thesis is about feature-based mobile robot navigation in unknown environments. The work is focusing on solving the problem of Simultaneous Localization and Mapping (SLAM) including feature extraction, estimation and complete map building solution. The robot platforms used in this work have the encoders for odometry and several laser scanners for exteroperceptive sensors. Simulations, real experiments and existing well known datasets are used in order to verify the performance of the developed algorithms. The algorithms developed in this thesis are features based meaning that instead of directly using raw sensory data geometric features are first extracted. This allows the filtering of noise from the sensors and the dynamics of the environment. More importantly, feature based approaches can reduce the amount of data needed to process, enabling the algorithms to scale in large environments. Another advantage of using features is the characteristic of better isolating informative patterns so that by exploiting specific knowledge of the environment, specialized algorithms can be easily adopted with different types of features. Several geometric features (points, lines, planes) are considered in this work. In this thesis, the relative mapping approach is adopted for solving the SLAM problem. This approach has many advantages. First, the mapping is decoupled from the estimation of robot pose so that the map estimation is independent from odometry errors. In other words, odometry errors do not affect and accumulate to the map estimate as in the absolute mapping approach. Second, the estimation does not involve the dynamics model of the robot which is identity in a relative mapping algorithm, meaning that only the observation model is considered in the estimation. A relative map has extra consistency problem compared to an absolute map. It is subject to the geometrical consistency problem which may generate an inconsistent absolute map after an transformation. However, having only observation model in the estimation gives an insight to separating nonlinearities from the iterative part of the estimator and thus minimize the effects of linearization errors. In this work, these subjects are investigated and reported. For some service robotic applications, especially for indoor office-like environments, we would like to have a fast, robust and lightweight approach which is capable to perform real-time SLAM on minimal hardware platforms. By exploiting some known knowledge about the structure of the environment, we claim to be able to achieve that goal. Particularly, the OrthoSLAM algorithm has been developed for building 2D and 3D maps. The algorithm adopts the Orthogonality assumption (or constraint) on the environment. First, the complexity of the SLAM algorithm is significantly reduced. Second, the linearization error from the robot heading uncertainty is removed and thus the map consistency is greatly improved. Implementations of the developed algorithms are tested on simulated data, on existing real world datasets and on robot platforms. Existing algorithms used in this work have been also reimplemented. The performance of the algorithms are analysed and compared on well chosen criteria. Particularly, the issues on computational complexity, linearization error, map consistency, map precision, realtime capability are investigated.