Large-scale Simultaneous Localization And Mapping Research Articles

LAMP 2.0: A Robust Multi-Robot SLAM System for Operation in Challenging Large-Scale Underground Environments

Search and rescue with a team of heterogeneous mobile robots in unknown and large-scale underground environments requires high-precision localization and mapping. This crucial requirement is faced with many challenges in complex and perceptually-degraded subterranean environments, as the onboard perception system is required to operate in off-nominal conditions (poor visibility due to darkness and dust, rugged and muddy terrain, and the presence of self-similar and ambiguous scenes). In a disaster response scenario and in the absence of prior information about the environment, robots must rely on noisy sensor data and perform Simultaneous Localization and Mapping (SLAM) to build a 3D map of the environment and localize themselves and potential survivors. To that end, this letter reports on a multi-robot SLAM system developed by team CoSTAR in the context of the DARPA Subterranean Challenge. We extend our previous work, LAMP, by incorporating a single-robot front-end interface that is adaptable to different odometry sources and lidar configurations, a scalable multi-robot front-end to support inter- and intra-robot loop closure detection for large scale environments and multi-robot teams, and a robust back-end equipped with an outlier-resilient pose graph optimization based on Graduated Non-Convexity. We provide a detailed ablation study on the multi-robot front-end and back-end, and assess the overall system performance in challenging real-world datasets collected across mines, power plants, and caves in the United States. We also release our multi-robot back-end datasets (and the corresponding ground truth), which can serve as challenging benchmarks for large-scale underground SLAM.

Large-Scale LiDAR SLAM with Factor Graph Optimization on High-Level Geometric Features.

Although visual SLAM (simultaneous localization and mapping) methods obtain very accurate results using optimization of residual errors defined with respect to the matching features, the SLAM systems based on 3-D laser (LiDAR) data commonly employ variants of the iterative closest points algorithm and raw point clouds as the map representation. However, it is possible to extract from point clouds features that are more spatially extended and more meaningful than points: line segments and/or planar patches. In particular, such features provide a natural way to represent human-made environments, such as urban and mixed indoor/outdoor scenes. In this paper, we perform an analysis of the advantages of a LiDAR-based SLAM that employs high-level geometric features in large-scale urban environments. We present a new approach to the LiDAR SLAM that uses planar patches and line segments for map representation and employs factor graph optimization typical to state-of-the-art visual SLAM for the final map and trajectory optimization. The new map structure and matching of features make it possible to implement in our system an efficient loop closure method, which exploits learned descriptors for place recognition and factor graph for optimization. With these improvements, the overall software structure is based on the proven LOAM concept to ensure real-time operation. A series of experiments were performed to compare the proposed solution to the open-source LOAM, considering different approaches to loop closure computation. The results are compared using standard metrics of trajectory accuracy, focusing on the final quality of the estimated trajectory and the consistency of the environment map. With some well-discussed reservations, our results demonstrate the gains due to using the high-level features in the full-optimization approach in the large-scale LiDAR SLAM.

RoomSLAM: Simultaneous Localization and Mapping With Objects and Indoor Layout Structure

This article presents RoomSLAM, a Simultaneous Localization and Mapping (SLAM) method for mobile robots in indoor environments where environments are modeled by points and quadrilaterals in 2D space. Points represent positions of semantic objects whereas quadrilaterals approximate the structural layout of the environment, namely rooms. The benefit of such modeling is threefold. Firstly, rooms are a logical way to partition a graph in large-scale SLAM. Secondly, rooms and objects reduce search space in data association. Lastly, the model contains a higher level of semantic, which is beneficial to autonomous robots whenever inter-room navigation is needed. The method was evaluated with two public datasets and the results were compared to those of ORBSLAM and RGBDSLAM.

Large-Scale Outdoor SLAM Based on 2D Lidar

For autonomous driving, it is important to navigate in an unknown environment. In this paper, we propose a fully automated 2D simultaneous localization and mapping (SLAM) system based on lidar working in large-scale outdoor environments. To improve the accuracy and robustness of the scan matching module, an improved Correlative Scan Matching (CSM) algorithm is proposed. For efficient place recognition, we design an AdaBoost based loop closure detection algorithm which can efficiently reject false loop closures. For the SLAM back-end, we propose a light-weight graph optimization algorithm based on incremental smoothing and mapping (iSAM). The performance of our system is verified on various large-scale datasets including our real-world datasets and the KITTI odometry benchmark. Further comparisons to the state-of-the-art approaches indicate that our system is competitive with established techniques.

Real-Time Direct Monocular SLAM With Learning-Based Confidence Estimation

Direct monocular simultaneous localization and mapping (SLAM) methods, for which the image intensity is used for tracking and mapping instead of sparse feature points, have gained in popularity in recent years. However, feature-based methods usually have more accurate camera localization results than most direct methods, though direct methods can work better in a textureless environment. To tackle the localization issue, we develop a novel real-time large-scale direct SLAM model, namely, GCP-SLAM, by integrating the learning-based confidence estimation into the depth fusion and motion tracking optimization. In GCP-SLAM, a random regression forest is trained off-line with pre-defined confidence measures for learning confidence and detecting the ground control points (GCPs). Then, the confidence value along with the selected GCPs is utilized for depth refinement and camera localization. Our proposed method is shown experimentally more reliable in tracking and relocalization than the previous state-of-the-art direct method when compared with feature-based and RGBD SLAMs.

Aerial Image Based Heading Correction for Large Scale SLAM in an Urban Canyon

This letter presents an effective solution for 3D urban map generation using aerial images as prior information for Simultaneous Localization and Mapping (SLAM). The research targets urban canyons where Global Positioning System (GPS) signals are highly sporadic and erroneous. As a solution for urban canyon mapping, this study proposes aerial image-based heading correction and kinematics considered odometry modeling. Aerial images often suffer from being significantly askew due to the height of structures and viewpoint changes, preventing direct integration with SLAM. However, we found that the direction of the structural edges was still valid, despite slanted building images. Making use of this property, this letter proposes a heading correction method using aerial images. As heading error is a critical factor in SLAM when mapping large areas, the proposed heading correction method substantially reduces estimation errors. Aiming for large urban area mapping, this letter also focuses on sensor modeling for odometry and GPS data. In odometry generation, mobile robot kinematics is considered by incorporating wheel diameter and base distance uncertainties into odometry covariance modeling. Results are presented from various types of urban areas over a path 28 km in length. For thorough validation, we ran the algorithm on three urban areas with differing degrees of GPS availability and structural complexity: a downtown area with medium complexity, a building complex, and the downtown area of a large city.

Affine-Invariant Geometric Constraints-Based High Accuracy Simultaneous Localization and Mapping

In this study we describe a new appearance-based loop-closure detection method for online incremental simultaneous localization and mapping (SLAM) using affine-invariant-based geometric constraints. Unlike other pure bag-of-words-based approaches, our proposed method uses geometric constraints as a supplement to improve accuracy. By establishing an affine-invariant hypothesis, the proposed method excludes incorrect visual words and calculates the dispersion of correctly matched visual words to improve the accuracy of the likelihood calculation. In addition, camera’s intrinsic parameters and distortion coefficients are adequate for this method. 3D measuring is not necessary. We use the mechanism of Long-Term Memory and Working Memory (WM) to manage the memory. Only a limited size of the WM is used for loop-closure detection; therefore the proposed method is suitable for large-scale real-time SLAM. We tested our method using the CityCenter and Lip6Indoor datasets. Our proposed method results can effectively correct the typical false-positive localization of previous methods, thus gaining better recall ratios and better precision.

Simulation study of a constant time hybrid approach for large scale terrain mapping using satellite stereo imagery

This work addresses the problem of performing large scale SLAM (Simultaneous Localization And Mapping) with satellite stereo imagery for terrain mapping, using a constant time estimation approach. The approach adopts the relative bundle adjustment approach (RBA) and integrates with it a particle-based framework to obtain a constant time probabilistic pose estimation model. The approach further uses a concept of fuzzy landmark-based similarity between poses to make common landmark identification across poses easier, especially when landmarks are sparsely encountered. In order to achieve robustness under varying environmental conditions, we use Speeded Up Robust Features (SURF) for computing spatial and temporal landmark correspondences across time steps. Finally, we use a fast loop closure approach to reduce drifts and obtain global pose estimates. For simulation study, the robot images are cropped from stereo-pair satellite images at different time steps incorporating errors in the robot’s control information. Extensive experimentation has been carried out to study the robot trajectories and the determination of Digital Elevation Model (DEM), with encouraging findings. We have also compared our work with 6D FastSLAM 2.0 (Thrun et al. (2005)) as well as Relative SLAM (RSLAM) due to Mei et al. (2010).

Contributions to metric-topological localization and mapping in mobile robotics

This thesis addresses the problem of localization and mapping in mobile robotics. The ability of a robot to build a map of an unknown environment from sensory information is required to perform self-localization and autonomous navigation, as a necessary condition to carry out more complex tasks. This problem has been widely investigated in the last decades, but the solutions presented have still important limitations, mainly to cope with large scale and dynamic environments, and to work in a wider range of conditions and scenarios. In this context, this thesis takes a step forward towards highly efficient localization and mapping.A first contribution of this work is a new mapping strategy that presents two key features: the lightweight representation of world metric information, and the organization of this metric map into a topological structure that allows efficient localization and map optimization. Regarding the first issue, a map is proposed based on planar patches which are extracted from range or RGB-D images. This plane-based map (PbMap) is particularly well suited for indoor scenarios, and has the advantage of being a very compact and still a descriptive representation which is useful to perform real-time place recognition and loop closure. These operations are based on matching planar features taking into account their geometric relationships. On the other hand, the abstraction of metric information is necessary to deal with large scale SLAM and with navigation in complex environments. For that, we propose to structure the map in a metric-topological structure which is dynamically organized upon the sensor observations. Also, a simultaneous localization and mapping (SLAM) system employing an omnidirectional RGB-D device which combines several structured-light sensors (Asus Xtion Pro Live) is presented. This device allows the quick construction of rich models of the environment at a relative low cost in comparison with previous alternatives. Our SLAM approach is based on a hierarchical structure of keyframes with a low level layer of metric information and several topological layers intended for large scale SLAM and navigation. This SLAM solution, which makes use of the metric-topological representation mentioned above, works at video frame rate obtaining highly consistent maps. Future research is expected on metric-topological-semantic mapping from the new sensor and the SLAM system presented here. Finally, an extrinsic calibration technique is proposed to obtain the relative poses of a combination of 3D range sensors, like those employed in the omnidirectional RGB-D device mentioned above. The calibration is computed from the observation of planar surfaces of a structured environment in a fast, easy and robust way, presenting qualitative and quantitative advantages with respect to previous approaches. This technique is extended to calibrate any combination of range sensors, including 2D and 3D range sensors, in any configuration. The calibration of such sets of sensors is interesting not only for mobile robots, but also for autonomous cars.

Effective cubature FastSLAM: SLAM with Rao-Blackwellized particle filter and cubature rule for Gaussian weighted integral

Simultaneous localization and mapping (SLAM) is a key technology for mobile robot autonomous navigation in unknown environments. While FastSLAM algorithm is a popular solution to the large-scale SLAM problem, it suffers from two major drawbacks: one is particle set degeneracy due to lack of measurements in proposal distribution of particle filter; the other is errors accumulation caused by inaccurate linearization of the nonlinear robot motion model and the environment measurement model. To overcome the problems, a new Jacobian-free cubature FastSLAM (CFastSLAM) algorithm is proposed in this paper. The main contribution of the algorithm lies in the utilization of third-degree cubature rule, which calculates the nonlinear transition density of Gaussian prior more accurately, to design an optimal proposal distribution of the particle filter and to estimate the Gaussian densities of the feature landmarks. On the basis of Rao-Blackwellized particle filter, the proposed algorithm is comprised by two main parts: in the first part, a cubature particle filter (CPF) is derived to localize the robot; in the second part, a set of cubature Kalman filters is used to estimate environment landmarks. The performance of the proposed algorithm is investigated and compared with that of FastSLAM2.0 and UFastSLAM in simulations and experiments. Results verify that the CFastSLAM improves the SLAM performance.

Large-Scale 6-DOF SLAM With Stereo-in-Hand

In this paper, we describe a system that can carry out simultaneous localization and mapping (SLAM) in large indoor and outdoor environments using a stereo pair moving with 6 DOF as the only sensor. Unlike current visual SLAM systems that use either bearing-only monocular information or 3-D stereo information, our system accommodates both monocular and stereo. Textured point features are extracted from the images and stored as 3-D points if seen in both images with sufficient disparity, or stored as inverse depth points otherwise. This allows the system to map both near and far features: the first provide distance and orientation, and the second provide orientation information. Unlike other vision-only SLAM systems, stereo does not suffer from ldquoscale driftrdquo because of unobservability problems, and thus, no other information such as gyroscopes or accelerometers is required in our system. Our SLAM algorithm generates sequences of conditionally independent local maps that can share information related to the camera motion and common features being tracked. The system computes the full map using the novel conditionally independent divide and conquer algorithm, which allows constant time operation most of the time, with linear time updates to compute the full map. To demonstrate the robustness and scalability of our system, we show experimental results in indoor and outdoor urban environments of 210 m and 140 m loop trajectories, with the stereo camera being carried in hand by a person walking at normal walking speeds of 4--5 km/h.

Divide and Conquer: EKF SLAM in $O(n)$

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In this paper, we show that <emphasis emphasistype="italic">all</emphasis> processes associated with the move-sense-update cycle of extended Kalman filter (EKF) Simultaneous Localization and Mapping (SLAM) can be carried out in time <emphasis emphasistype="italic">linear</emphasis> with the number of map features. We describe <emphasis emphasistype="italic">Divide and Conquer</emphasis> SLAM, which is an EKF SLAM algorithm in which the computational complexity per step is reduced from <formula formulatype="inline"><tex Notation="TeX">$O(n^2)$</tex></formula> to <formula formulatype="inline"><tex Notation="TeX"> $O(n)$</tex></formula>, and the total cost of SLAM is reduced from <formula formulatype="inline"><tex Notation="TeX">$O(n^3)$</tex> </formula> to <formula formulatype="inline"><tex Notation="TeX">$O(n^2)$</tex></formula>. Unlike many current large-scale EKF SLAM techniques, this algorithm computes a solution without relying on approximations or simplifications (other than linearizations) to reduce computational complexity. Also, estimates and covariances are available when needed by data association without any further computation. Furthermore, as the method works most of the time in local maps, where angular errors remain small, the effect of linearization errors is limited. The resulting vehicle and map estimates are more precise than those obtained with standard EKF SLAM. The errors with respect to the true value are smaller, and the computed state covariance is consistent with the real error in the estimation. Both simulated experiments and the Victoria Park dataset are used to provide evidence of the advantages of this algorithm. </para>

Large-Scale SLAM Building Conditionally Independent Local Maps: Application to Monocular Vision

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Simultaneous localization and mapping (SLAM) algorithms based on local maps have been demonstrated to be well suited for mapping large environments as they reduce the computational cost and improve the consistency of the final estimation. The main contribution of this paper is a novel submapping technique that does not require independence between maps. The technique is based on the intrinsic structure of the SLAM problem that allows the building of submaps that can share information, remaining <emphasis emphasistype="italic">conditionally independent</emphasis>. The resulting algorithm obtains local maps in constant time during the exploration of new terrain and recovers the global map in linear time after simple loop closures without introducing any approximations besides the inherent extended Kalman filter linearizations. The memory requirements are also linear with the size of the map. As the algorithm works in a covariance form, well-known data-association techniques can be used in the usual manner. We present experimental results using a handheld monocular camera, building a map along a closed-loop trajectory of 140 m in a public square, with people and other clutter. Our results show that the combination of conditional independence, which enables the system to share the camera and feature states between submaps, and local coordinates, which reduce the effects of linearization errors, allow us to obtain precise maps of large areas with pure monocular SLAM in real time. </para>