Three main paradigms of simultaneous localization and mapping (SLAM) problem

This paper presents a sensor fusion strategy applied for Simultaneous Localization and Mapping (SLAM) in dynamic environments. The designed approach consists of two features: (i) the first one is a fusion module which synthesizes line segments obtained from laser rangefinder and line features extracted from monocular camera. This policy eliminates any pseudo segments that appear from any momentary pause of dynamic objects in laser data. (ii) The second characteristic is a modified multi-sensor point estimation fusion SLAM (MPEF-SLAM) that incorporates two individual Extended Kalman Filter (EKF) based SLAM algorithms: monocular and laser SLAM. The error of the localization in fused SLAM is reduced compared with those of individual SLAM. Additionally, a new data association technique based on the homography transformation matrix is developed for monocular SLAM. This data association method relaxes the pleonastic computation. The experimental results validate the performance of the proposed sensor fusion and data association method.

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.

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.

Regardless of the tremendous progress, a truly general purpose pipeline for Simultaneous Localization and Mapping (SLAM) remains a challenge. We investigate the reported failure of state of the art (SOTA) SLAM techniques on egocentric videos. We find that the dominant 3D rotations, low parallax between successive frames, and primarily forward motion in egocentric videos are the most common causes of failures. The incremental nature of SOTA SLAM, in the presence of unreliable pose and 3D estimates in egocentric videos, with no opportunities for global loop closures, generates drifts and leads to the eventual failures of such techniques. Taking inspiration from batch mode Structure from Motion (SFM) techniques, we propose to solve SLAM as an SFM problem over the sliding temporal windows. This makes the problem well constrained. Further, we propose to initialize the camera poses using 2D rotation averaging, followed by translation averaging before structure estimation using bundle adjustment. This helps in stabilizing the camera poses when 3D estimates are not reliable. We show that the proposed SLAM technique, incorporating the two key ideas works successfully for long, shaky egocentric videos where other SOTA techniques have been reported to fail. Qualitative and quantitative comparisons on publicly available egocentric video datasets validate our results.

Computer vision approaches are increasingly used in mobile robotic systems, since they allow to obtain a very good representation of the environment by using low-power and cheap sensors. In particular it has been shown that they can compete with standard solutions based on laser range scanners when dealing with the problem of simultaneous localization and mapping (SLAM), where the robot has to explore an unknown environment while building a map of it and localizing in the same map. We present a package for simultaneous localization and mapping in ROS (Robot Operating System) using a monocular camera sensor only. Experimental results in real scenarios as well as on standard datasets show that the algorithm is able to track the trajectory of the robot and build a consistent map of small environments, while running in near real-time on a standard PC.

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 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).

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) 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.

Map learning and self-localization based only on a perception of an environment’s structure are fundamental cognitive capacities required for intelligent robot’s to realize true autonomy. Simultaneous localization and mapping (SLAM) is an effective technique for such robots, as it addresses the problem of incrementally building an environment map from noisy sensory data and tracking the robot’s pose with the built map. While the Rao-Blackwellized particle filter (RBPF) is a popular SLAM technique, it tends to accumulate errors introduced by inaccurate linearization of the SLAM nonlinear function. Accordingly, RBPF-SLAM will usually fail to close large loops when applied to large-scale environments. To overcome this drawback, a new Jacobian-free RBPF-SLAM algorithm is derived in this paper. The main contribution of the algorithm lies in the utilization of the 5th-order conjugate unscented transform, which calculates the SLAM transition density up to the 5th order, to give a better distribution of the particle filter and discover local features and landmarks. The performance of the proposed SLAM is investigated and compared with that of FastSLAM2.0 and UFastSLAM in both indoor and outdoor experiments. The results verify that the proposed algorithm improves the SLAM performance in large-scale environments.

The millimeter-wave (mmWave) communication technology, which employs large-scale antenna arrays, enables inherent sensing capabilities. Simultaneous localization and mapping (SLAM) can utilize channel multipath angle estimates to realize integrated sensing and communication design in 6G communication systems. However, existing works have ignored the significant overhead required by the mmWave beam management when implementing SLAM with angle estimates. This study proposes a joint beam management and SLAM design that utilizes the strong coupling between the radio map and channel multipath for simultaneous beam management, localization, and mapping. In this approach, we first propose a hierarchical sweeping and sensing service design. The path angles are estimated in the hierarchical sweeping, enabling angle-based SLAM with the aid of an inertial measurement unit (IMU) to realize sensing service. Then, feature-aided tracking is proposed that utilizes prior angle information generated from the radio map and IMU. Finally, a switching module is introduced to enable flexible switching between hierarchical sweeping and feature-aided tracking. Simulations show that the proposed joint design can achieve sub-meter level localization and mapping accuracy (with an error < 0.5 m). Moreover, the beam management overhead can be reduced by approximately 40% in different wireless environments.

Simultaneous localization and mapping (SLAM) is an important problem to solve in robotics theory in order to build truly autonomous mobile robots. This work presents a novel method for implementing a SLAM system based on a single camera sensor. The SLAM with a single camera, or monocular SLAM, is probably one of the most complex SLAM variants. In this case, a single camera, which is freely moving through its environment, represents the sole sensor input to the system. The sensors have a large impact on the algorithm used for SLAM. Cameras are used more frequently, because they provide a lot of information and are well adapted for embedded systems: they are light, cheap and power-saving. Nevertheless, and unlike range sensors, which provide range and angular information, a camera is a projective sensor providing only angular measurements of image features. Therefore, depth information (range) cannot be obtained in a single step. In this case, special techniques for feature system-initialization are needed in order to enable the use of angular sensors (as cameras) in SLAM systems. The main contribution of this work is to present a novel and robust scheme for incorporating and measuring visual features in filtering-based monocular SLAM systems. The proposed method is based in a two-step technique, which is intended to exploit all the information available in angular measurements. Unlike previous schemes, the values of parameters used by the initialization technique are derived directly from the sensor characteristics, thus simplifying the tuning of the system. The experimental results show that the proposed method surpasses the performance of previous schemes.

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved?

Simultaneous localization and mapping (SLAM) consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications and witnessing a steady transition of this technology to industry. We survey the current state of SLAM and consider future directions. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved?

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.