Simultaneous Localization and Mapping with Basic Cognitive Understanding of Environments

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.

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.

In this work, we tested Simultaneous localization and mapping (SLAM) about mobile robots in indoor environment, where all experiments were conducted based on the Robot Operating System (ROS). The urban search and rescue(USAR) environment was build in the ROS simulation tool Gazebo, and our car was used to test hector SLAM in Gazebo. The rplidar A1 single-line lidar was used for 2D laser scan matching data acquisition in the practical experiments and the indoor map was built by using the open source algorithms gmapping, karto SLAM, hector SLAM software package for indoor SLAM, which can get the indoor grid maps in ROS graphical tool RVIZ. The experimental results of the three open source algorithms show that the mobile robot for simultaneous localization and mapping (SLAM) is feasible, and high-precision grid maps can be constructed.

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.

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.

Simultaneous Localization and Mapping (SLAM) enable autonomous mobile robots to build a map of an unknown environment while estimating their own position within it. Unstructured terrain, dynamic environmental conditions, and sensor limitations, often encountered in off-road areas can limit the effectiveness of SLAM. This review analyzes SLAM methodologies applicable to off-road mobile robots, categorizing them into LiDAR-based, visual, multi-sensor fusion, and learning-based or semantic approaches. Each category is examined in terms of algorithmic principles, performance characteristics, and suitability for varying terrain and environmental conditions. Furthermore, to assess the performance of these categories evaluation metrics are utilized, including accuracy, drift rate, robustness, computational efficiency, and benchmarking datasets. The comparative analysis highlights trade-offs between geometric precision, adaptability, and computational demands, with multi-sensor fusion and semantic integration. Real-time operation under limited onboard computation, scalability to large unstructured terrains, resilience in GPS-denied and feature-scarce environments, and integration with autonomous navigation systems are some of the identified research gaps. The findings emphasize the need for hybrid, computation-aware SLAM frameworks and standardized off-road benchmarks to accelerate the deployment of reliable autonomous systems in challenging outdoor environments.

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.

An artificial intelligent grey wolf optimizer (GWO)-assisted resampling scheme is applied to the Rao-Blackwellized particle filter (RBPF) in the simultaneous localization and mapping (SLAM). By doing this, we can make the diversity of the particles resampling and then obtain a better localization accuracy and fast convergence to realize indoor mobile robot SLAM. In addition, we propose an adaptive local data association (Range-SLAM) scheme to improve the computational efficiency for the algorithm of the nearest neighbor (NN) data association in the iteration of the RBPF prediction. Through the experiment and simulations, the proposed SLAM schemes have fast convergence, accuracy, and heuristics. Therefore, the improved RBPF and new data association schemes presented in this paper can provide a feasible method for the indoor mobile robot SLAM.

Recently, considerable attention has been focused on the investigation of the use of wireless sensor networks (WSN) to drive mobile robot for efficient exploration in unknown environments. The process of navigation depends heavily on estimating the position of the features provided by sensor within the entire environment. This paper proposes a novel navigation algorithm based on RF wireless sensor networks to simultaneous localization and mapping (SLAM) for an indoor autonomous mobile robot. Triangulation localization method is employed to make the robot locates itself and knows its pose. A simple yet efficient two-step particle filter is applied for map building. Simulation and experimental results show that good localization can be achieved using the proposed method.

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.

A Simultaneous Localization and Mapping (SLAM) system must be robust to support long-term mobile vehicle and robot applications. However, camera and LiDAR based SLAM systems can be fragile when facing challenging illumination or weather conditions which degrade the utility of imagery and point cloud data. Radar, whose operating electromagnetic spectrum is less affected by environmental changes, is promising although its distinct sensor model and noise characteristics bring open challenges when being exploited for SLAM. This paper studies the use of a Frequency Modulated Continuous Wave radar for SLAM in large-scale outdoor environments. We propose a full radar SLAM system, including a novel radar motion estimation algorithm that leverages radar geometry for reliable feature tracking. It also optimally compensates motion distortion and estimates pose by joint optimization. Its loop closure component is designed to be simple yet efficient for radar imagery by capturing and exploiting structural information of the surrounding environment. Extensive experiments on three public radar datasets, ranging from city streets and residential areas to countryside and highways, show competitive accuracy and reliability performance of the proposed radar SLAM system compared to the state-of-the-art LiDAR, vision and radar methods. The results show that our system is technically viable in achieving reliable SLAM in extreme weather conditions on the RADIATE Dataset, for example, heavy snow and dense fog, demonstrating the promising potential of using radar for all-weather localization and mapping.

Detecting glass in Simultaneous Localisation and Mapping

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) 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.

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?