Graph-based Simultaneous Localization Research Articles

Graph-Based Simultaneous Localization and Bias Tracking

Graph-Based Simultaneous Localization and Bias Tracking

Situation-Aware Drivable Space Estimation for Automated Driving

An automated vehicle (AV) must always have a correct representation of the drivable space to position itself accurately and operate safely. To determine the drivable space, current research focuses on single sources of information, either using pre-computed high-definition maps, or mapping the environment online with sensors such as LiDARs or cameras. However, each of these information sources can fail, some are too costly, and maps could be outdated. In this work a new method for situation-aware drivable space (SDS) estimation combining multiple information sources is proposed, which is also suitable for AVs equipped with inexpensive sensors. Depending on the situation, semantic information of sensed objects is combined with domain knowledge to estimate the drivability of the space surrounding each object (e.g. traffic light, another vehicle). These estimates are modeled as probabilistic graphs to account for the uncertainty of information sources, and an optimal spatial configuration of their elements is determined via graph-based simultaneous localization and mapping (SLAM). To investigate the robustness of SDS towards potentially unreliable sensors and maps, it has been tested in a simulation environment and real world data. Results on different use cases (e.g. straight roads, curved roads, and intersections) show considerable robustness towards unreliable inputs, and the recovered drivable space allows for accurate in-lane localization of the AV even in extreme cases where no prior knowledge of the road network is available.

Unsupervised Balanced Covariance Learning for Visual-Inertial Sensor Fusion

Incorporating multi-sensor, in filter-based as well as graph-based simultaneous localization and mapping (SLAM), relies on the uncertainties involved in each measurement. Proper covariance estimation is thus critical to balance confidence levels among sensors. Despite its importance, traditional covariance approximation mostly relied on first order derivative or fixed measurement covariance and therefore tended to be error-prone, and even heuristic. Recently, deep learning for uncertainty estimation yielded meaningful performance, but applied to a single sensor in a supervised manner. Unlike the traditional supervised manner, we introduce an unsupervised loss for uncertainty modeling, to learn uncertainty without needing ground truth covariance as a label. Most important, we overcome the limitation of learning a single sensor's uncertainty by introducing a way of balancing uncertainty between different sensor modalities. In doing so, we alleviate the uncertainty balancing issue between sensors that has often been encountered in the multi-sensor SLAM application. Targeting covariance learning for visual odometry, particularly with regard to the integration of inertial sensors, the proposed uncertainty learning method was validated in a visual-inertial odometry application over the public datasets under artificial visual and inertial degradations to mimic harsh environment.

Bridge Inspection Using Unmanned Aerial Vehicle Based on HG-SLAM: Hierarchical Graph-Based SLAM

With the increasing demand for autonomous systems in the field of inspection, the use of unmanned aerial vehicles (UAVs) to replace human labor is becoming more frequent. However, the Global Positioning System (GPS) signal is usually denied in environments near or under bridges, which makes the manual operation of a UAV difficult and unreliable in these areas. This paper addresses a novel hierarchical graph-based simultaneous localization and mapping (SLAM) method for fully autonomous bridge inspection using an aerial vehicle, as well as a technical method for UAV control for actually conducting bridge inspections. Due to the harsh environment involved and the corresponding limitations on GPS usage, a graph-based SLAM approach using a tilted 3D LiDAR (Light Detection and Ranging) and a monocular camera to localize the UAV and map the target bridge is proposed. Each visual-inertial state estimate and the corresponding LiDAR sweep are combined into a single subnode. These subnodes make up a “supernode” that consists of state estimations and accumulated scan data for robust and stable node generation in graph SLAM. The constraints are generated from LiDAR data using the normal distribution transform (NDT) and generalized iterative closest point (G-ICP) matching. The feasibility of the proposed method was verified on two different types of bridges: on the ground and offshore.

CPL-SLAM: Efficient and Certifiably Correct Planar Graph-Based SLAM Using the Complex Number Representation

In this article, we consider the problem of planar graph-based simultaneous localization and mapping (SLAM) that involves both poses of the autonomous agent and positions of observed landmarks. We present complex (CPL)-SLAM, an efficient and certifiably correct algorithm to solve planar graph-based SLAM using the complex number representation. We formulate and simplify planar graph-based SLAM as the maximum likelihood estimation on the product of unit complex numbers, and relax this nonconvex quadratic complex optimization problem to convex complex semidefinite programming (SDP). Furthermore, we simplify the corresponding complex SDP to Riemannian staircase optimization (RSO) on the complex oblique manifold that can be solved with the Riemannian trust region method. In addition, we prove that the SDP relaxation and RSO simplification are tight as long as the noise magnitude is below a certain threshold. The efficacy of this work is validated through applications of CPL-SLAM and comparisons with existing state-of-the-art methods on planar graph-based SLAM, which indicates that our proposed algorithm is capable of solving planar graph-based SLAM certifiably, and is more efficient in numerical computation and more robust to measurement noise than existing state-of-the-art methods. The C++ code for CPL-SLAM is available at https://github.com/MurpheyLab/CPL-SLAM.

Multi-Objective Optimization of Loop Closure Detection Parameters for Indoor 2D Simultaneous Localization and Mapping.

Aiming at addressing the issues related to the tuning of loop closure detection parameters for indoor 2D graph-based simultaneous localization and mapping (SLAM), this article proposes a multi-objective optimization method for these parameters. The proposed method unifies the Karto SLAM algorithm, an efficient evaluation approach for map quality with three quantitative metrics, and a multi-objective optimization algorithm. More particularly, the evaluation metrics, i.e., the proportion of occupied grids, the number of corners and the amount of enclosed areas, can reflect the errors such as overlaps, blurring and misalignment when mapping nested loops, even in the absence of ground truth. The proposed method has been implemented and validated by testing on four datasets and two real-world environments. For all these tests, the map quality can be improved using the proposed method. Only loop closure detection parameters have been considered in this article, but the proposed evaluation metrics and optimization method have potential applications in the automatic tuning of other SLAM parameters to improve the map quality.

Robust 3-Dimension Point Cloud Mapping in Dynamic Environment Using Point-Wise Static Probability-Based NDT Scan-Matching

Graph-based simultaneous localization and mapping (SLAM) is one of the methods to generate point cloud maps which are used for various applications in autonomous vehicles. Graph-based SLAM represents the pose of the vehicle as a node and the odometry between two different nodes as an edge. Among the edge generating methods, scan matching, light detection and ranging (LiDAR) based method, can provide an accurate pose between two nodes based on the high distance accuracy of the LiDAR. However, the point cloud in real driving situations contains numerous moving objects, which degrade the scan-matching performance. Therefore, this article defines the static probability which means the likelihood that an acquired point is from a static object, and proposes the weighted normal distribution transformation (NDT), which is achieved by modifying NDT. Weighted NDT is a scan-matching algorithm which can reflect the static probability of each point as a weight. The odometry from the weighted NDT is utilized for graph construction to generate a robust point cloud map even in a dynamic environment. Finally, the proposed algorithm was compared with the existing object removal algorithms in two areas: dynamic object classification and scan-matching performance. Based on the scan-matching results, the accuracy of the point cloud map generated by the proposed algorithm was evaluated with a reference map using high-performance global navigation satellite system (GNSS). It was confirmed that the proposed algorithm has higher classification accuracy and lower scan-matching error compared with other dynamic object removal methods. The proposed algorithm was able to generate a point cloud map, despite the presence of many dynamic objects, that was similar to a map generated in the absence of dynamic objects in the same environment.

Autonomous Indoor Exploration Via Polygon Map Construction and Graph-Based SLAM Using Directional Endpoint Features

In this paper, a novel 2-D laser-based autonomous exploration approach for mobile robots is proposed, which is based on a novel polygon map construction approach and graph-based simultaneous localization and mapping (SLAM) with directional endpoint features. This approach is composed of three modules: graph-based SLAM using directional endpoint features, polygon map construction, and exploration. Different from existing approaches in the field of 2-D SLAM, the newly proposed 2-D graph-SLAM is based on 3-D “directional endpoint” features; on this basis, a well-known data structure “circular-doubly linked list” is applied to construct a novel polygon map for navigation. Note that it is efficient for circular-doubly linked list to initialize and update the polygon map. In addition, we propose a new information entropy calculation approach to quantify the entropy of the polygon map. Then for each candidate goal, we could obtain corresponding information gain and make next decision through collision detection. Comparative experimental results with respect to the well-known Gmapping and Karto SLAM are presented to show superior performance of the proposed graph-based SLAM. The autonomous exploration experiments in the office and hallway environments show the effectiveness of the proposed approach for robotic mapping and exploration tasks. Note to Practitioners —This paper is motivated by the challenges of autonomous exploration for mobile robots. We suggest a novel autonomous exploration approach through directional endpoint feature-based graph simultaneous localization and mapping (SLAM) and polygon map construction. This newly proposed approach does not require any artificial landmark or underlying occupancy grid map, which is easy to implement. The experiments are carried out in the office and hallway environments, including graph-based SLAM and autonomous exploration for mobile robots. The experimental results show the effectiveness of the proposed autonomous exploration framework. In the future research, we will address autonomous exploration in more challenging dynamic environments.

A two-level optimized graph-based simultaneous localization and mapping algorithm

PurposeBecause submaps including a subset of the global map contain more environmental information, submap-based graph simultaneous localization and mapping (SLAM) has been studied by many researchers. In most of those studies, helpful environmental information was not taken into consideration when designed the termination criterion of the submap construction process. After optimizing the graph, cumulative error within the submaps was also ignored. To address those problems, this paper aims to propose a two-level optimized graph-based SLAM algorithm.Design/methodology/approachSubmaps are updated by extended Kalman filter SLAM while no geometric-shaped landmark models are needed; raw laser scans are treated as landmarks. A more reasonable criterion called the uncertainty index is proposed to combine with the size of the submap to terminate the submap construction process. After a submap is completed and a loop closure is found, a two-level optimization process is performed to minimize the loop closure error and the accumulated error within the submaps.FindingsSimulation and experimental results indicate that the estimated error of the proposed algorithm is small, and the maps generated are consistent whether in global or local.Practical implicationsThe proposed method is robust to sparse pedestrians and can be adapted to most indoor environments.Originality/valueIn this paper, a two-level optimized graph-based SLAM algorithm is proposed.

A graph optimization approach for motion estimation using inertial measurement unit data

This study presents a novel approach for processing motion data from a six-degree-of-freedom inertial measurement unit (IMU). Trajectory estimation through double integration of acceleration measurements results in the generation and accumulation of multiple errors. Existing IMU-based measurement methods often use constrained initial and final states to resolve these errors. The constraints on the initial and final states lead to a uniform distribution of the accumulated errors throughout the calculated trajectory so that they cancel each other. We develop a generalized method that can incorporate the constraints from the measurements of intermediate states. The proposed approach is inspired by graph-based simultaneous localization and mapping processes from robotics research. We tested the proposed method with simulated and actual IMU data and found that our method estimates trajectories more accurately than conventional methods with acceptably higher computational costs.

Online semantic mapping of logistic environments using RGB-D cameras

Automated guided vehicles require spatial representations of their working spaces in order to ensure safe navigation and carry out high-level tasks. Typically, these models are given by geometric maps. Even though these enable basic robotic navigation, they off-the-shelf lack the availability of task-dependent information required to provide services. This article presents a semantic mapping approach augmenting existing geometric representations. Our approach demonstrates the automatic annotation of map subspaces on the example of warehouse environments. The proposals of an object recognition system are integrated in a graph-based simultaneous localization and mapping framework and eventually propagated into a global map representation. Our system is experimentally evaluated in a typical warehouse consisting of common object classes expected for this type of environment. We discuss the novel achievements and motivate the contribution of semantic maps toward the operation of automated guided vehicles in the context of Industry 4.0.

Graph-Based Simultaneous Localization and Mapping: Computational Complexity Reduction on a Multicore Heterogeneous Architecture

This article deals with the computational complexity issue of graphbased simultaneous localization and mapping (SLAM). SLAM allows a robot that is navigating in an unknown environment to build a map of this environment while simultaneously determining the robot pose on this map. Graph-based SLAM is a smoothing method that uses a graph to represent and solve the SLAM problem. We first propose a graph construction that takes advantage of the incremental and sparse characteristics of graph-based SLAM. This incremental construction is exploited to perform several algorithmic optimizations. Second, we present a study of using a heterogeneous architecture to implement the graph-based SLAM algorithm. Indeed, the emergence of recent heterogeneous embedded architectures should lead to a great advance in the design of embedded systems-based robotics applications. As a result of this study, an algorithm-architecture mapping is proposed for a central processing unit-graphics processing unit (CPU-GPU)-based architecture. The study also investigates how this kind of architecture can speed up graph-based SLAM by offloading some critical compute-intensive tasks of the algorithm on the GPU. Some common data sets are used to compare our implementations to the state of the art.

Mapping underwater ship hulls using a model-assisted bundle adjustment framework

This paper reports on a model-assisted bundle adjustment (BA) framework in which visually-derived features are fused with an underlying three-dimensional (3D) mesh provided a priori. By using an approach inspired by the expectation–maximization (EM) class of algorithms, we introduce a hidden binary label for each visual feature that indicates if that feature is considered part of the nominal model, or if the feature corresponds to 3D structure that is absent from the model. Therefore, in addition to improved estimates of the feature locations, we can identify visual features that correspond to foreign structure on the ship hull. We show that this framework is a special case of the Gaussian max-mixtures framework, which can be efficiently incorporated into state-of-the-art graph-based simultaneous localization and mapping (SLAM) solvers.In addition, the precision of our bundle adjustment framework allows the identification of structural deviations between 3D structure inferred from bundle-adjusted camera imagery and the prior model. These structural deviations are clustered into shapes, which allow us to fuse camera-derived structure back into the 3D mesh. This augmented model can be used within a 3D photomosaicing pipeline, providing a visually intuitive 3D reconstruction of the ship hull. We evaluate our pipeline using the Bluefin Robotics hovering autonomous underwater vehicle (HAUV) surveying the SS Curtiss, where a 3D mesh derived from computer aided design (CAD) drawings serves as the prior model. In addition to more consistent visual reconstructions, we can update the prior mesh with 3D information corresponding to underwater structure, such as biofouling or manually-placed cylindrical shapes with known dimensions.

준공 BIM 구축을 위한 Graph-based SLAM 기반의 실내공간 3차원 지도화 연구

현재 국내 대부분의 토목 건축 구조물이 BIM 정보가 부재한 상황에서 준공 BIM(as-built BIM)의 수요가 점차 증가하고 있다. 준공 BIM 구축을 위한 공간자료 취득에는 고밀도의 포인트 클라우드를 생성할 수 있는 레이저 스캐너가 주로 활용되고 있다. 하지만 기존의 고정식 스캔 시스템은 이동이 번거롭고, 정밀한 위치 선정이 필요 하며, 스캔 자료 정합을 위해 별도의 표지를 설치하거나 공액점을 추출하는 과정이 필요하다. 본 연구에서는 수작업을 최소화하기 위해 기존의 고정식 스캔 시스템을 대체할 수 있는 이동식 스캔 시스템을 제안하고자 하며, 기반 기술로 graph-based SLAM을 적용하였다. 테스트 장비는 총 세 개의 2차원 스캐너를 탑재하고 있으며, 중앙의 한 개는 수평으로 설치되어 graph 구축을 통한 이동경로취득에 사용되었고, 좌우 두 개는 수직으로 설치되어 시스템 진행의 연직 방향으로 주변 구조물에 대한 3차원 스캔 정보 취득에 사용되었다. 개발된 graph-based SLAM은 이동경로 상에 누적된 위치오차를 해소하기 위한 loop closure 처리 방법으로 Adaboost 기계학습을 적용하였다. 이는 특히 본 연구에서 사용한 장비와 같이 기계학습을 위한 다수의 feature 정보를 제공할 수 있는 멀티 스캐너 시스템에 적합한 방식이며, 두 실내공간을 대상으로 한 테스트에서 단일 스캐너 대비 false positive rate를 각각 7.9% 및 13.6%까지 줄일 수 있었다. 최종적으로 연구대상지역의 2차원 및 3차원 지도 구축을 통해 개발된 graph-based SLAM의 효용성을 확인하였다. In Korea, the absence of BIM use in existing civil structures and buildings is driving a demand for as-built BIM. As-built BIMs are often created using laser scanners that provide dense 3D point cloud data. Conventional static laser scanning approaches often suffer from limitations in their operability due to the difficulties in moving the equipment, the selection of scanning location, and the requirement of placing targets or extracting tie points for registration of each scanned point cloud. This paper aims at reducing the manual effort using a kinematic 3D laser scanning system based on graph-based simultaneous localization and mapping (SLAM) for continuous indoor mapping. The robotic platform carries three 2D laser scanners: the front scanner is mounted horizontally to compute the robot's trajectory and to build the SLAM graph; the other two scanners are mounted vertically to scan the profiles of surrounding environments. To reduce the accumulated error in the trajectory of the platform through loop closures, the graph-based SLAM system incorporates AdaBoost loop closure approach, which is particularly suitable for the developed multi-scanner system providing more features than the single-scanner system for training. We implemented the proposed method and evaluated it in two indoor test sites. Our experimental results show that the false positive rate was reduced by 13.6% and 7.9% for the two dataset. Finally, the 2D and 3D mapping results of the two test sites confirmed the effectiveness of the proposed graph-based SLAM.

Retinal slit lamp video mosaicking.

PurposeTo this day, the slit lamp remains the first tool used by an ophthalmologist to examine patient eyes. Imaging of the retina poses, however, a variety of problems, namely a shallow depth of focus, reflections from the optical system, a small field of view and non-uniform illumination. For ophthalmologists, the use of slit lamp images for documentation and analysis purposes, however, remains extremely challenging due to large image artifacts. For this reason, we propose an automatic retinal slit lamp video mosaicking, which enlarges the field of view and reduces amount of noise and reflections, thus enhancing image quality.MethodsOur method is composed of three parts: (i) viable content segmentation, (ii) global registration and (iii) image blending. Frame content is segmented using gradient boosting with custom pixel-wise features. Speeded-up robust features are used for finding pair-wise translations between frames with robust random sample consensus estimation and graph-based simultaneous localization and mapping for global bundle adjustment. Foreground-aware blending based on feathering merges video frames into comprehensive mosaics.ResultsForeground is segmented successfully with an area under the curve of the receiver operating characteristic curve of 0.9557. Mosaicking results and state-of-the-art methods were compared and rated by ophthalmologists showing a strong preference for a large field of view provided by our method.ConclusionsThe proposed method for global registration of retinal slit lamp images of the retina into comprehensive mosaics improves over state-of-the-art methods and is preferred qualitatively.

Nonlinear factor recovery for long-term SLAM

For long-term operations, graph-based simultaneous localization and mapping (SLAM) approaches require nodes to be marginalized in order to control the computational cost. In this paper, we present a method to recover a set of nonlinear factors that best represents the marginal distribution in terms of Kullback–Leibler divergence. The proposed method, which we call nonlinear factor recovery (NFR), estimates both the mean and the information matrix of the set of nonlinear factors, where the recovery of the latter is equivalent to solving a convex optimization problem. NFR is able to provide either the dense distribution or a sparse approximation of it. In contrast to previous algorithms, our method does not necessarily require a global linearization point and can be used with any nonlinear measurement function. Moreover, we are not restricted to only using tree-based sparse approximations and binary factors, but we can include any topology and correlations between measurements. Experiments performed on several publicly available datasets demonstrate that our method outperforms the state of the art with respect to the Kullback–Leibler divergence and the sparsity of the solution.

Generalized graph SLAM: Solving local and global ambiguities through multimodal and hyperedge constraints

Research in Graph-based Simultaneous Localization and Mapping has experienced a recent trend towards robust methods. These methods take the combinatorial aspect of data association into account by allowing decisions of the graph topology to be made during optimization. The Generalized Graph Simultaneous Localization and Mapping framework presented in this work can represent ambiguous data on both local and global scales, i.e. it can handle multiple mutually exclusive choices in registration results and potentially erroneous loop closures. This is achieved by augmenting previous work on multimodal distributions with an extended graph structure using hyperedges to encode ambiguous loop closures. The novel representation combines both hyperedges and multimodal Mixture of Gaussian constraints to represent all sources of ambiguity in Simultaneous Localization and Mapping. Furthermore, a discrete optimization stage is introduced between the Simultaneous Localization and Mapping frontend and backend to handle these ambiguities in a unified way utilizing the novel representation of Generalized Graph Simultaneous Localization and Mapping, providing a general approach to handle all forms of outliers. The novel Generalized Prefilter method optimizes among all local and global choices and generates a traditional unimodal unambiguous pose graph for subsequent continuous optimization in the backend. Systematic experiments on synthetic datasets show that the novel representation of the Generalized Graph Simultaneous Localization and Mapping framework with the Generalized Prefilter method, is significantly more robust and faster than other robust state-of-the-art methods. In addition, two experiments with real data are presented to corroborate the results observed with synthetic data. Different general strategies to construct problems from real data, utilizing the full representational power of the Generalized Graph Simultaneous Localization and Mapping framework are also illustrated in these experiments.

Multiplicative Extended Kalman Filter for Relative Rotorcraft Navigation

This paper details the fundamentals of a new approach to navigation for aerial vehicles in confined indoor environments without access to global-position measurements. The approach departs from the common practice of navigating within a globally referenced map, and it instead keeps the position and yaw states relative to the current node in the map. The approach combines elements of graph-based simultaneous localization and mapping with a multiplicative extended Kalman filter. The filter provides accurate state estimates at a fast rate and provides the information necessary for a simultaneous localization and mapping algorithm to maintain a pose graph. Specific details for the relative multiplicative extended Kalman filter are provided. The relative estimation approach is validated with hardware flight tests, and results are compared to motion capture ground truth data. In addition, flight-test results using estimates in the control loop are provided.

Graph-based SLAM: A Survey

Graph-based simultaneous localization and mapping(SLAM) is currently a hot research topic in the field of robotics.Frame-to-frame alignment,loop closure detection and graph optimization are three main aspects in graph-based SLAM.For each aspect,the key techniques and current progress are discussed,and the difficult problems and the possible solutions are also analyzed.Finally,the potential future issues and research trends are explored.

Information-theoretic compression of pose graphs for laser-based SLAM

In graph-based simultaneous localization and mapping (SLAM), the pose graph grows over time as the robot gathers information about the environment. An ever growing pose graph, however, prevents long-term mapping with mobile robots. In this paper, we address the problem of efficient information-theoretic compression of pose graphs. Our approach estimates the mutual information between the laser measurements and the map to discard the measurements that are expected to provide only a small amount of information. Our method subsequently marginalizes out the nodes from the pose graph that correspond to the discarded laser measurements. To maintain a sparse pose graph that allows for efficient map optimization, our approach applies an approximate marginalization technique that is based on Chow–Liu trees. Our contributions allow the robot to effectively restrict the size of the pose graph. Alternatively, the robot is able to maintain a pose graph that does not grow unless the robot explores previously unobserved parts of the environment. Real-world experiments demonstrate that our approach to pose graph compression is well suited for long-term mobile robot mapping.