D-SLAM: Decoupled Localization and Mapping for Autonomous Robots

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.

The Simultaneous Localization and Mapping (SLAM) problem for mobile robots aims at consistently building a map of an unknown environment while simultaneously determining its position or location within this map. From a control-theory viewpoint, it is somehow analogous to simultaneously estimating the states and output map of the system. In the robotic based engineering applications, SLAM is arguably considered a solved problem on a theoretical and conceptual level, but still it requires considerable maturity on a practical level [16][22]. The state-of-the-art SLAM algorithms require computationally powerful processors, expensive sensors with dense feature extraction and multiple sensors for uncertainty reduction [21]. An approach to the SLAM problem using minimal sensing information is still lacking in both theoretical and practical aspects. This paper highlights the data acquisition & association issues with different sensors like odometric & infra red sensors used in SLAM application due to addition of random Gaussian noise leading to errors & uncertainty in mapping processes [17][22]. Simple filtering techniques using average filtering & Kalman filtering have been implemented on the acquired data to go for comparative analysis of sensor performance & its impact on SLAM process towards reduction in uncertainty or minimal shift in landmark extraction [19].

Simultaneous localization and mapping (SLAM) has been investigated in the field of robotics for two decades, as it is considered to be an effective method for solving the positioning and mapping problem in a single framework. In the SLAM community, the Extended Kalman Filter (EKF) based SLAM and particle filter SLAM are the most mature technologies. After years of development, graph-based SLAM is becoming the most promising technology and a lot of progress has been made recently with respect to accuracy and efficiency. No matter which SLAM method is used, loop closure is a vital part for overcoming the accumulated errors. However, in 2D Light Detection and Ranging (LiDAR) SLAM, on one hand, it is relatively difficult to extract distinctive features in LiDAR scans for loop closure detection, as 2D LiDAR scans encode much less information than images; on the other hand, there is also some special mapping scenery, where no loop closure exists. Thereby, in this paper, instead of loop closure detection, we first propose the method to introduce extra control network constraint (CNC) to the back-end optimization of graph-based SLAM, by aligning the LiDAR scan center with the control vertex of the presurveyed control network to optimize all the poses of scans and submaps. Field tests were carried out in a typical urban Global Navigation Satellite System (GNSS) weak outdoor area. The results prove that the position Root Mean Square (RMS) error of the selected key points is 0.3614 m, evaluated with a reference map produced by Terrestrial Laser Scanner (TLS). Mapping accuracy is significantly improved, compared to the mapping RMS of 1.6462 m without control network constraint. Adding distance constraints of the control network to the back-end optimization is an effective and practical method to solve the drift accumulation of LiDAR front-end scan matching.

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).

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.

Simultaneous localization and mapping (SLAM) becomes popular in the autonomous robot research community. SLAM requests a mobile robot, placed at an unknown location in an unknown environment, to incrementally build a consistent map of this environment while simultaneously determining its location within this map. The odometer data is helpful but the noisy error accumulates. Assistant with measurements from the other sensors, SLAM improves the accuracy by filtering. The extended Kalman filter is the most used method to feedback the error. In this thesis, a monocular vision based SLAM for a wheeled robot is proposed. A single web camera is the only measurement input to correct the odometer data. Extract features and match them based on the scale invariant feature transform (SIFT). Absence of the feature depth information is the key issue for the monocular vision in SLAM. The inverse depth parametrization method solves the problem by present the position in inverse coordinate with some reasonable assumptions. Convergence of the feature position is more easily achieved by the EKF due to the linearity. Combining the odometer data with the inverse depth method, this system provides an accurate map by tests of the indoor image sequence.

In this paper, we automate the traditional problem of Simultaneous Localization and Mapping (SLAM) by interleaving planning for exploring unknown environments by a mobile robot. We denote such planned SLAM systems as SPLAM (Simultaneous Planning Localization and Mapping). The main aim of SPLAM is to plan paths for the SLAM process such that the robot and map uncertainty upon execution of the path remains minimum and tractable. The planning is interleaved with SLAM and hence the terminology SPLAM. While typical SPLAM routines find paths when the robot traverses amidst known regions of the constructed map, herein we use the SPLAM formulation for an exploration like situation. Exploration is carried out through a frontier based approach where we identify multiple frontiers in the known map. Using Randomized Planning techniques we calculate various possible trajectories to all the known frontiers. We introduce a novel strategy for selecting frontiers which mimics Fast SLAM, selects a trajectory for robot motion that will minimize the map and robot state covariance. By using a Fast SLAM like approach for selecting frontiers we are able to decouple the robot and landmark covariance resulting in a faster selection of next best location, while maintaining the same kind of robustness of an EKF based SPLAM framework. We then compare our results with Shortest Path Algorithm and EKF based Planning. We show significant reduction in covariance when compared with shortest frontier first approach, while the uncertainties are comparable to EKF-SPLAM albeit at much faster planning times.

A Geese PSO tuned fuzzy supervisor for EKF based solutions of simultaneous localization and mapping (SLAM) problems in mobile robots

Mapping and self-localization in unknown environments are fundamental capabilities in many robotic applications. These tasks typically involve the identification of objects as unique features or landmarks, which requires the objects both to be detected and then assigned a unique identifier that can be maintained when viewed from different perspectives and in different images. The data association and simultaneous localization and mapping (SLAM) problems are, individually, well-studied in the literature. But these two problems are inherently tightly coupled, and that has not been well-addressed. Without accurate SLAM, possible data associations are combinatorial and become intractable easily. Without accurate data association, the error of SLAM algorithms diverge easily. This paper proposes a novel nonparametric pose graph that models data association and SLAM in a single framework. An algorithm is further introduced to alternate between inferring data association and performing SLAM. Experimental results show that our approach has the new capability of associating object detections and localizing objects at the same time, leading to significantly better performance on both the data association and SLAM problems than achieved by considering only one and ignoring imperfections in the other.

Mobile robot could navigate in unknown environment autonomously with the help of Simultaneous Localization and Mapping (SLAM). Recently, SLAM based on information matrix enjoys much popularity since it is naturally sparse. However, the computational burden related to information matrix balloons with respect to the increase of the mapped landmarks. In this paper, by considering the features of information matrix, we present a novel method which wipes off nearly half of the elements in information matrix. The errors that come from sparsification decrease apparently by loop-closure. Furthermore, the relationship between sparsification and SLAM accuracy is analyzed theoretically. A large scale simulation and experiment conducted on a real robot suggest that the technique is effective for a robot's SLAM in real-world applications.

Simultaneous localization and mapping (SLAM) for mobile robots has attracted research interests in the past two decades. Recent years, Rao-Blackwellized particle filter (RBPF) approach proved to be an effective means to estimate the full SLAM posterior. However, most of the SLAM practices are implemented in static environment. To navigate in dynamic environment, model based approaches have been implemented. The approaches require modeling, classification, and data association. Modeling and classification often require prior knowledge of the environment and are less robust in highly complex environment. To obviate modeling, classification and data association in dynamic environment, a solution framework based on Rao-Blackwellised genetic algorithmic filter (RBGAF) is extended for recovering the full SLAM posterior, using raw sensor measurements. The resultant extended RBGAF-SLAM recovers the negative sensor information during SLAM so that the moving objects can be efficiently identified in the particle maps. This approach permits the implementation of SLAM without prior knowledge of the environment and the static environment assumption. Simulations and experimental results obtained in an outdoor environment using a laser measurement system are presented to demonstrate the method's effectiveness.

To navigate successfully, a mobile robot must be able to estimate the spatial relationships of the objects of interest accurately. A SLAM (Simultaneous Localization and Mapping) system employs its sensor data to build incrementally a map of an unknown environment and to localize itself in the map simultaneously. Thanks to recent advances in computer vision and cheaper cameras, vision sensors have become popular to solve SLAM problems. The proposed bearing-only SLAM system requires only a single camera which is simple and affordable for the navigation of domestic robots such as autonomous lawn-mowers and vacuum cleaners. Existing approaches to bearing-only SLAM require the readings from an odometer to estimate the robot locations prior to landmark initialization. This chapter presents a new 2-dimensional bearing-only SLAM system that relies only on the bearing measurements from a single camera. Our proposed system does not require any other sensors like range sensors or wheel encoders. The trade-off is that it requires the robot to be able to move in a straight line for a short while to initialize the landmarks. Once the landmark positions are estimated, localization becomes easy. The induced map created by our method is only determined up to a scale factor as only bearing information is used (no range or odometry information). All the object coordinates in the map multiplied by a scale factor would not change the bearing values.

When UAV is implementing the simultaneous location and mapping (SLAM) problem, the environment where UAV is flying exist unavoidable solid or moving obstacles, which threaten the flying safety and the completeness of SLAM mission. To conquer this problem, an improved artificial potential field algorithm is proposed to simultaneously accomplish obstacle avoidance of UAV and SLAM mission based on a cost function containing the distance from UAV to the goal and from UAV to the obstacles. Concerning the built UAV plane motion model, this algorithm is simulated and tested. The result shows that the proposed algorithm is effective to avoid the obstacles for UAV SLAM.

In the study of indoor simultaneous localization and mapping (SLAM) problems using a stereo camera, two types of primary features—point and line segments—have been widely used to calculate the pose of the camera. However, many feature-based SLAM systems are not robust when the camera moves sharply or turns too quickly. In this paper, an improved indoor visual SLAM method to better utilize the advantages of point and line segment features and achieve robust results in difficult environments is proposed. First, point and line segment features are automatically extracted and matched to build two kinds of projection models. Subsequently, for the optimization problem of line segment features, we add minimization of angle observation in addition to the traditional re-projection error of endpoints. Finally, our model of motion estimation, which is adaptive to the motion state of the camera, is applied to build a new combinational Hessian matrix and gradient vector for iterated pose estimation. Furthermore, our proposal has been tested on EuRoC MAV datasets and sequence images captured with our stereo camera. The experimental results demonstrate the effectiveness of our improved point-line feature based visual SLAM method in improving localization accuracy when the camera moves with rapid rotation or violent fluctuation.

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.