A NOVEL DATA ASSOCIATION TECHNIQUE TO IMPROVE RUN-TIME EFFICIENCY OF SLAM ALGORITHMS

Autonomous indoor mobile robots are very promising application of robotics. In order to realize autonomous navigation, a robot that enters an unknown environment needs to reconstruct a consistent map of the environment and estimate its pose with respect to the map, simultaneously. This problem is well known as the Simultaneous Localization And Mapping (SLAM) problem, which has attracted a lot of interest from researchers in past few decades. The most popular approaches towards SLAM problem are usually developed based on the probabilistic methods, such as Extended Kalman Filter (EKF) SLAM, particle filter SLAM, and maximum likelihood SLAM. In recent years, a robust technology named “Scan Matching” plays a very important role in solving the SLAM problem. By matching sensor scans that are taken from different poses, the scan matching method can efficiently estimate the rigid transformation of the robot between two poses. Due to the fact that the exploring sensors are usually very accurate and robust, scan matching is very efficient for mobile robot to localize itself with respect to the given reference scans or maps. Although vision based approaches are getting more and more popular in SLAM research field, vision sensors are sensitive to the unpredictable variations of environment, such as the change of the lighting condition. Besides, most vision based solutions construct sparse feature points based maps which are not sufficient for robot autonomous navigation. Therefore Laser Range Finder (LRF) based scan matching method and fast indoor SLAM framework are still widely desired in consideration of the robustness of LRF towards environment changes. Another widely adopted sensor is Inertial Measurement Unit (IMU) which provides measurements of accelerations and rotating rates at the same time. In consideration of cost efficiency, Micro Electrical Mechanical Systems (MEMS) technology based IMU is preferable in consuming grade applications as well as robotic researches. However, the measurements of low-cost MEMS-IMUs are usually corrupted by various types of noises. Thus, a calibration work to compress noises is necessary before the usage of MEMS-IMU. The main contributions of this study are consisted of three main parts: Fast and Robust Scan Matching Approach: Various scan matching methods have been introduced. And the most widely used methods, Iterative Closest Point (ICP) and its variants, have been deeply investigated and modified to obtain better performance. New preprocessors as well as new distance metric for association process have been proposed to enhance the robustness of ICP. A new framework of incremental scan matching has been developed to fulfill the scan matching task in large indoor loops. Line-segment based EKF-SLAM with Slope and Edge Detection: In this contribution, planar line-segment based EKF-SLAM has been well investigated at the very beginning. Then, the slope and edge in structured indoor environment have been modelled to line-segments and merged into the EKF-SLAM framework. In order to overcome…

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.

The most common way to deal with the uncertainty present in noisy sensorial perception and action is to model the problem with a probabilistic framework. Maximum likelihood estimation is a well-known estimation method used in many robotic and computer vision applications. Under Gaussian assumption, the maximum likelihood estimation converts to a nonlinear least squares problem. Efficient solutions to nonlinear least squares exist and they are based on iteratively solving sparse linear systems until convergence. In general, the existing solutions provide only an estimation of the mean state vector, the resulting covariance being computationally too expensive to recover. Nevertheless, in many simultaneous localization and mapping (SLAM) applications, knowing only the mean vector is not enough. Data association, obtaining reduced state representations, active decisions and next best view are only a few of the applications that require fast state covariance recovery. Furthermore, computer vision and robotic applications are in general performed online. In this case, the state is updated and recomputed every step and its size is continuously growing, therefore, the estimation process may become highly computationally demanding. This paper introduces a general framework for incremental maximum likelihood estimation called SLAM++, which fully benefits from the incremental nature of the online applications, and provides efficient estimation of both the mean and the covariance of the estimate. Based on that, we propose a strategy for maintaining a sparse and scalable state representation for large scale mapping, which uses information theory measures to integrate only informative and non-redundant contributions to the state representation. SLAM++ differs from existing implementations by performing all the matrix operations by blocks. This led to extremely fast matrix manipulation and arithmetic operations used in nonlinear least squares. Even though this paper tests SLAM++ efficiency on SLAM problems, its applicability remains general.

The process of building a map with a mobile robot is known as the Simultaneous Localization and Mapping (SLAM) problem, and is considered essential for achieving true autonomy. The best existing solutions to the SLAM problem are based on probabilistic techniques, mainly derived from the basic Bayes Filter. A recent approach is the use of Rao-Blackwellized particle filters. The FastSLAM solution factorizes the Bayes SLAM posterior using a particle filter to estimate over the possible paths of the robot and several independent Kalman Filters attached to each particle to estimate the location of landmarks conditioned to the robot path. Although there are several successful implementations of this idea, there is a lack of applications to indoor environments where the most common feature is the line segment corresponding to straight walls. This paper presents a novel factorization, which is the dual of the existing FastSLAM one, that decouples the SLAM into a map estimation and a localization problem, using a particle filter to estimate over maps and a Kalman Filter attached to each particle to estimate the robot pose conditioned to the given map. We have implemented and tested this approach, analyzing and comparing our solution with the FastSLAM one, and successfully building feature based maps of indoor environments.

This paper addresses a novel approach to the solution of the simultaneous localization and mapping (SLAM) problem bared on a neuro evolutionary optimization (NeoSLAM) method. The proposed algorithm first casts SLAM as a global optimization problem using the cost function which represents the quality of robot pose trajectory and the feature positions in world coordinate frame. In our algorithm, the neural network trained to estimate the pose difference of the two consecutive positions accurately from the corresponding sensor data and the previous pose difference. The cost function is formulated as the importance of the full SLAM assumptions of EKF. Evolutionary programming (EP) is used to evolve the neural model that is most consistent with the actual data measurement. Prediction and correction is simultaneously performed in our neural model that combines both the motion model and sensor model. By way of learning and evolution, our algorithm does not need prior assumption on the motion and sensor models, and therefore shows a robust performance regardless of the actual noise type. Further, our method can generate an accurate map even without the data association step, paving the way to deal with practical applications. Both the simulation and real experimental results conducted made various environments and noise/sensor types demonstrate that NeoSLAM ensures a consistently robust and accurate performance.

This paper describes a Takagi–Sugeno (T–S) fuzzy model adopted solution to the simultaneous localization and mapping (SLAM) problem with two-sensor data association (TSDA) method. Nonlinear process model and observation model are formulated as pseudolinear models and rewritten with a composite model whose local models are linear according to T–S fuzzy model. Combination of these local state estimates results in global state estimate. This paper introduces an extended TSDA (ETSDA) method for the SLAM problem in mobile robot navigation based on an interior point linear programming (LP) approach. Simulation results are given to demonstrate that the ETSDA method has low computational complexity and it is more accurate than the existing single-scan joint probabilistic data association method. The above system is implemented and simulated with Matlab to claim that the proposed method yet finds a better solution to the SLAM problem than the conventional extended Kalman filter–SLAM algorithm.

Simultaneous localization and mapping (SLAM) is a process wherein a robotic system acquires a map of its environment while simultaneously localizing itself relative to this map. A common solution to the SLAM problem involves the use of the extended Kalman filter (EKF). This filter is used to calculate the posterior probability of the robot pose and map given observations and control inputs. From the EKF, one estimates the mean and error covariance of the robot pose and map features by using nonlinear motion and observation models. In this article, the conditions required for the convergence of the errors in the EKF estimates obtained by linearizing the nonlinear system equations are studied and applied to the SLAM problem. In particular, the observability condition of the system describing the typical SLAM problem is studied. Numerical studies are carried out to compare the accuracy of the EKF estimates for a representative SLAM formulation which is not observable with a SLAM formulation that satisfies the observability condition.

This paper presents an algorithm for the simultaneous localization and mapping (SLAM) problem. Inspired by the basic idea of the fast SLAM which separates the robot pose estimation problem and mapping problem, we use the particle filter (PF) to estimate the pose of individual robot and use the multi-dimensional scaling (MDS), one of the distance mapping method, to find the relative coordinates of landmarks toward the robot. We apply the proposed algorithm to not only the single robot SLAM, but also the multi-robot SLAM. Experimental results demonstrate the effectiveness of the proposed algorithm over the Fast SLAM. The accuracy of the Fast SLAM and that of our proposed SLAM are almost matched with less particles.

Simultaneous localization and mapping (SLAM) is an essential capability for Unmanned Ground Vehicles (UGVs) travelling in unknown environments where globally accurate position data as GPS is not available. It is an important topic in the autonomous mobile robot research. This paper presents an Adaptive De-centralized Cooperative Vision-based SLAM solution for multiple UGVs, using the Adaptive Covariance Intersection (ACI) supported by a stereo vision sensor. In recent years, SLAM problem has gotten a specific consideration, the most commonly used approaches are the EKF-SLAM algorithm and the FAST-SLAM algorithm. The primary, which requires an accurate process and an observation model, suffers from the linearization problem. The last mentioned is not suitable for real-time implementation. In our work, the Visual SLAM (VSLAM) problem could be solved based on the Smooth Variable Structure Filter (SVSF) is proposed. This new filter is robust and stable to modelling uncertainties making it suitable for UGV localization and mapping problem. This new strategy retains the near optimal performance of the SVSF when applied to an uncertain system, it has the added benefit of presenting a considerable improvement in the robustness of the estimation process. All UGVs will add data features sorted by the ACI that estimate position on the global map. This solution gives, as a result, a large reliable map constructed by a group of UGVs plotted on it. This paper presents a Cooperative SVSF-VSLAM algorithm that contributes to solve the Adaptive Cooperative Vision SLAM problem for multiple UGVs. The algorithm was implemented on three mobile robots Pioneer 3-AT, using stereo vision sensors. Simulation results show eciency and give an advantage to our proposed algorithm, compared to the Cooperative EKF-VSLAM algorithm mainly concerning the noise quality. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

미지의 영역에서 작업을 수행하고자 하는 이동로봇은 주변의 지도가 없을 뿐만 아니라 자신의 위치도 알 수 없다. 이러한 환경의 극복을 위해 가장 많이 쓰이는 방법이 SLAM(Simultaneous Localization And Mapping)이다. SLAM 분야에서 가장 많이 쓰이는 방법은 EKF (Extended Kalman Filter) 기반의 SLAM이다. 최적의 센서 융합 기법이지만 odometeric error 등을 보상하기 위해서는 복잡한 과정이 점차 증가하게 된다. 사람은 SLAM 방식을 이용하여 낯선 장소에서 마음속의 지도를 쉽게 작성하지만 로봇의 경우 SLAM을 수행하는 것은 매우 어렵고 시간이 오래 걸린다는 단점이 생기는 것이 다. 이러한 단점의 보완을 위하여 본 논문에서는 대칭모형 SLAM(M-SLAM)을 제안한다. M-SLAM은 대칭에 사용할 모형을 미리 정하고 센서로 받아들인 데이터를 모형과 비교하여 대칭된 모형을 맵에 적용시켜서 작업의 양을 줄이는 방법이다. M-SLAM은 적은 특징점을 이용하여 선택된 대칭 도형과의 유사성 판별을 이용하는 방법이므로 특징점이 적은 거리센서에 사용하기 적합한 특성을 가지고 있다고 할 수 있다. 특징점이 적어도 된다는 장점은 SLAM의 시간을 크게 줄여 줄수 있다. 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 odometeric 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).

This chapter presents a stereo vision application to Mobile Robotics. In particular we deal with the problem of simultaneous localization and mapping (SLAM) (Dissanayake et al., 2001; Montemerlo et al., 2002) and propose a stereo vision-based technique to solve it (Gil et al., 2006). The problem of SLAM is of paramount importance in the mobile robotics community, since it copes with the problem of incrementally building a map of the environment while simultaneously localizing the robot within this map. Building a map of the environment is a fundamental task for autonomous mobile robots, since the maps are required for different higher level tasks, such as path planning or exploration. It is certainly an ability necessary to achieve a true autonomous operation of the robot. In consequence, this problem has received significant attention in the past two decades. The SLAM problem is inherently a hard problem, because noise in the estimate of the robot pose leads to noise in the estimate of the map and viceversa. The approach presented here is feature based, since it concentrates on a number of points extracted from images in the environment which are used as visual landmarks. The map is formed by the 3D position of these landmarks, referred to a common reference frame. The visual landmarks are extracted by means of the Scale Invariant Feature Transform (SIFT) (Lowe, 2004). A rejection technique is applied in order to concentrate on a reduced set of highly distinguishable, stable features. The SIFT transform detects distinctive points in images by means of a difference of gaussian function (DoG) applied in scale space. Next, a descriptor is computed for each detected point, based on local image information at the characteristic scale (Lowe, 2004). We track detected SIFT features along consecutive frames obtained by a stereo camera and select only those features that appear to be stable from different views. Whenever a feature is selected, we compute a more representative feature model given the previous observations. This model allows to improve the Data Association within the landmarks in the map and, in addition, permits to reduce the number of landmarks that need to be maintained in the map. The visual SLAM approach is applied within a Rao-Blackwellized particle filter (Montemerlo et al., 2002; Grisetti et al., 2005). In this chapter we propose two relevant contributions to the visual SLAM solution. First, we present a new mechanism to deal with the data association problem for the case of visual landmarks. Second, our approach actively tracks landmarks prior to its integration in the map. As a result, we concentrate on a small set of stable landmarks and incorporate them in the map. With this approach, our map typically consists of a reduced number of landmarks O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m

SLAM (Simultaneous Localization and Mapping) is a relevant topic of research and development in the field of robotics and computer vision. SLAM finds wide applications in various areas such as autonomous navigation of intelligent robots, solving problems in augmented and virtual reality, UAVs, and other systems. In recent years, SLAM has made significant progress due to the gradual development of its algorithms, the use of advanced sensors, and improvements in computational power of computers. The subject of this study is modern methods of real-time simultaneous localization and mapping. The goal of the research is to model the developed algorithm for constructing maps of the surrounding environment and determining the position and orientation of the intelligent robot in space in real-time using ROS packages. The purpose of this article is to demonstrate the results of combining SLAM methods and developing new approaches to solve simultaneous localization and mapping problems. In order to achieve the set objectives, a collaboration of laser scanning (2D LRF) and depth image reconstruction (RGB-D) methods was utilized for simultaneous localization and mapping of the intelligent robot and construction of a 2.5D environment map. The obtained results are promising and demonstrate the potential of the integrated SLAM methods, which collaborate to ensure accurate execution of simultaneous localization and mapping for intelligent robots in real-time mode. The proposed method allows for considering obstacle heights in constructing the map of the surrounding environment while requiring less computational power. In conclusion, this approach expands technologies without replacing existing working propositions and enables the use of modern methods for comprehensive detection and recognition of the surrounding environment through an efficient localization and mapping approach, providing more accurate results with fewer resources utilized.

The of the simultaneous localisation and mapping (SLAM) problem has been one of the notable successes of the robotics community. SLAM has been formulated and solved as a theoretical problem in a number of different forms. SLAM has also been implemented in a number of different domains from indoor robots to outdoor, underwater, and airborne systems. At a theoretical and conceptual level, SLAM can now be considered a solved problem. However, substantial issues remain in practically realizing more general SLAM solutions and notably in building and using perceptually rich maps as part of a SLAM algorithm. This paper describes the autonomous underwater vehicle (AUV) kinematic and sensor models, it overviews the basic theoretical solution to the extended kalman filter (EKF) SLAM problem, it also describes the way-point guidance based on line of sight (LOS). In this paper, it has been shown through Matlab simulation that the vehicle is able to localize its position using features that it observes In the environment andl at thle same time map thlose features. The vehicle is expected to follow a pre-defined sinusoidal path.

Maps are needed for a wide range of applications. In the context of mobile robotics, the map learning problem under uncertainty is often referred to as the simultaneous localization and mapping problem. In this paper, we aim at exploiting already available information such as OpenStreetMap data within the Simultaneous Localization and Mapping (SLAM) problem. We achieve this by relating the information about buildings with the perceptions of the robot and generate constraints for the pose graph-based formulation of the SLAM problem. In addition to that, we present a way to select target locations for the robot so that by going there, the robot can expect to reduce its own pose uncertainty. This localizability information is generated directly from OpenStreetMap data and supports active localization. We implemented and evaluated our approach using real-world data taken in urban environments. Our experiments suggest that we are able to relate the newly built maps with information from OpenStreetMap with the laser range finder data from the robot and in this way improve the map quality. The extension to graph-based SLAM provides better aligned maps and adds only a marginal computational overhead. Furthermore, we illustrate that the localizability information is useful to evaluate the ability to localize the robot given a trajectory.

In this research simultaneous localization and mapping (SLAM) problem of unmanned systems which has emerged in last decade is identified by detecting SLAM algorithms particularly in air vehicle platforms and particle filter based SLAM implementation of aerial systems is first introduced as well. Regarding to survey consequences the variety of SLAM applications span from parametric filters such as Unscented Kalman Filter, Extended Kalman Filter to nonparametric such as Particle Filter and concerning diversity of vision based approaches that aims up level control and variety of sensors that unmanned vehicles carry a taxonomy is a requirement for better comprehension of SLAM performances. Although it is not aimed to compare performance of all SLAM methods for problem of Airborne-SLAM (A-SLAM) navigation in GNSS denied environment the scan of indexed papers suggests via providing brief background such as Kalman and particle filter based Simultaneous Localization and Mapping (SLAM) approach formulations or simulations that best SLAM algorithm can only be identified in reference to the scenario which differs in environment, platform, vehicle, sensor…etc. while key findings of Unscented Kalman Filter (UKF), Extended Kalman Filter (EKF) and Particle Filter (PF) Based A-SLAM structures give that Particle Filter (PF) Based A-SLAM may be superior to others in some scenarios principally depending on particle number.