A Benchmark for Multi-Modal LiDAR SLAM with Ground Truth in GNSS-Denied Environments

Simultaneous localization and mapping (SLAM) represents a crucial algorithm in the autonomous navigation of ground vehicles. Several studies were conducted to improve the SLAM algorithm using various sensors and robot platforms. However, only a few works have focused on applications inside low-illuminated featureless tunnel environments. In this work, we present an improved SLAM algorithm using wheel encoder data from an autonomous ground vehicle (AGV) to obtain robust performance in a featureless tunnel environment. The improved SLAM system uses FAST-LIO2 LiDAR SLAM as the baseline algorithm, and the additional wheel encoder sensor data are integrated into the baseline SLAM structure using the extended Kalman filter (EKF) algorithm. The EKF algorithm is used after the LiDAR odometry estimation and before the mapping process of FAST-LIO2. The prediction step uses the wheel encoder and inertial measurement unit (IMU) data, while the correction step uses the FAST-LIO2 LiDAR state estimation. We used an AGV to conduct experiments in flat and inclined terrain sections in a tunnel environment. The results showed that the mapping and the localization process in the SLAM algorithm was greatly improved in a featureless tunnel environment considering both inclined and flat terrains.

The accuracy and stability of front-end point cloud registration algorithms are crucial for the mapping and localization precision in laser SLAM (simultaneous localization and mapping) systems. Traditional point-to-line and point-to-plane iterative closest point (ICP) registration algorithms, widely used in SLAM front ends, often suffer from low efficiency, significant data dependency during the matching process, and a propensity for local optima. This registration method exhibits a more pronounced local optimum issue in large-scale SLAM mapping, thereby diminishing matching accuracy and increasing reliance on initial values. To address these limitations, this paper introduces NI-LIO, a novel SLAM algorithm that integrates ICP with normal distributions transform (NDT) to enhance localization accuracy, computational efficiency and robustness. By combining the precision of ICP with the robustness of NDT, the proposed algorithm significantly improves system stability and localization accuracy. The analysis of mapping and localization experiments indicates a significant reduction in errors compared to traditional SLAM algorithms, with experiments showing a REMS value decrease of over 20%. Compared to ALOAM, FAST_LIO2 and Lego-LOAM algorithms, the new NI-LIO algorithm shows improvements in both accuracy and stability, enabling the construction of a more precise and consistent global map. This algorithm exhibits excellent adaptability to various environments.

In this paper, we propose a comparison of open-source LiDAR and Inertial Measurement Unit (IMU)-based Simultaneous Localization and Mapping (SLAM) approaches for 3D robotic mapping. The analyzed algorithms are often exploited in mobile robotics for autonomous navigation but have not been evaluated in terms of 3D reconstruction yet. Experimental tests are carried out using two different autonomous mobile platforms in three test cases, comprising both indoor and outdoor scenarios. The 3D models obtained with the different SLAM algorithms are then compared in terms of density, accuracy, and noise of the point clouds to analyze the performance of the evaluated approaches. The experimental results indicate the SLAM methods that are more suitable for 3D mapping in terms of the quality of the reconstruction and highlight the feasibility of mobile robotics in the field of autonomous mapping.

Right invariant extended Kalman filter (RIEKF) based simultaneous localization and mapping (SLAM) proposed recently has shown to be able to produce more consistent SLAM estimates as compared with traditional EKF based SLAM methods, including some improved EKF SLAM methods such as observability constrained-EKF (OC-EKF) SLAM. Latest results have demonstrated that its performance is very close to optimization based SLAM algorithms such as iSAM. In this paper, we propose to use RIEKF SLAM algorithm in active SLAM where both the predicted SLAM results for choosing control actions and the actual estimated SLAM results applying the selected control actions are computed using RIEKF algorithms. The advantages over traditional EKF based active SLAM are the more accurate and consistent predicted uncertainty estimates which result in robustness of the active SLAM algorithm. The advantages over optimization based active SLAM is the reduced computational cost. Simulation results are presented to validate the advantages of the proposed algorithm³.

This paper describes an approach that combines the navigation data given by a Doppler Velocity Logs (DVL), the MTi Motion Reference Unit (MRU) and a Mechanically Scanned Imaging Sonar (MSIS) as a principal sensor to efficiently solve underwater Simultaneous Localization and Mapping (SLAM) problem in structured environments such as marine platforms, harbors, or dams, etc. The MSIS has been chosen of its capacity to produce a rich representation of the environment. In recent years, to solve the SLAM Autonomous Underwater Vehicle (AUV) problem, very few solutions have been proposed. Our contribution has introduced a method based on the Nonlinear H-infinity filter \((NH\infty )\) to solve the SLAM-AUV problem. In this work, the \(NH\infty \)-SLAM algorithm is implemented to construct a map in partially structured environments and localize the AUV within this map. The data-set used in this paper are taken from the experiments carried out in a marina located in the Costa Brava (Spain) with the Ictineu AUV which is necessary to test different SLAM algorithms. The validation of the proposed algorithm through simulation in offline is presented and compared to the EKF-SLAM algorithm. The \(NH\infty \)-SLAM algorithm provides an accurate estimate than EKF-SLAM and good results were obtained.

Several works have focused on Simultaneous Localization and Mapping (SLAM), which is a topic that has been studied for more than a decade to meet the needs of robots to navigate in an unknown environment. SLAM is an essential perception functionality in several applications, especially in robotics and autonomous vehicles. RGB-D cameras are among the sensors commonly used with recent SLAM algorithms. They provide an RGB image and the associated depth map, making it possible to solve scale drift with less complexity and create a dense 3D environment representation. Many RGB-D SLAM algorithms have been studied and evaluated on publicly available datasets without considering sensor specifications or image acquisition modes that could improve or decrease localization accuracy. In this work, we deal with indoor localization, taking into account the sensor specifications. In this context, our contribution is a deep experimental study to highlight the impact of the sensor acquisition modes on the localization accuracy, and a parametric optimization protocol for a precise localization in a given environment. Furthermore, we apply the proposed protocol to optimize a depth-related parameter of the SLAM algorithm. The study is based on a publicly available dataset in an indoor environment with a depth sensor. The reconstruction results’ analysis is founded on the study of different metrics involving translational and rotational errors. These metrics errors are compared with those obtained with a state-of-the-art stereo vision-based SLAM algorithm.

This paper presents a simulator to simulating two key problems-underwater perception and autonomous manipulation o In the simulator a controller-observer strategy is proposed. A new SLAM (Simultaneous Localization and Mapping) algorithm - UKF (Unscented Kalman Filter) is used to estimate the state of UVMS (Underwater Vehicle Manipulator Systems) and the target object. 3D Simulation results show that the new SLAM algorithm can get the optimal estimation of state of UVMS and the target object, and the end-effector of manipulator can reach the desired object and the accuracy is centimeter level.

With the development of automatic driving cars, simultaneous localization and mapping (SLAM) becomes a widely discussed topic. Loop closure detection, as an important module in lidar slam, can provide strong constraint for localization and mapping. In classical lidar SLAM algorithm, traditional loop closure detection method often fails to find out the matching nodes, or even has false positive results, especially in challenging environment like the crossroads or sharp turns. In this paper, a lidar SLAM loop closure detection method using adjacent nodes to check the reliability is introduced. A series of experiments is implemented to verify the effectiveness of the method.

Navigation system for orchard spraying robot based on 3D LiDAR SLAM with NDT_ICP point cloud registration

Simultaneous localization and mapping (SLAM) is the key technology to realize self-localization of autonomous vehicles. However, the lidar SLAM with traditional loop closure detection cannot obtain reliable accuracy and real-time under large scale scene due to the accumulated error of point cloud registration. In this paper, a hybrid loop closure detection (HLCD) method based on spatial location and appearance similarity is proposed and introduced into lidar SLAM technology. An experimental test is implemented on the platform of autonomous vehicle to verify the correctness and feasibility of theoretical design. Experimental results show that the proposed method can effectively reduce the cumulative deviation and time consuming.

This work concerns the study of 6DSLAM algorithms with an application of robotic mobile mapping systems. The architecture of the 6DSLAM algorithm is designed for evaluation of different data registration strategies. The algorithm is composed of the iterative registration component, thus ICP (Iterative Closest Point), ICP (point to projection), ICP with semantic discrimination of points, LS3D (Least Square Surface Matching), NDT (Normal Distribution Transform) can be chosen. Loop closing is based on LUM and LS3D. The main research goal was to investigate the semantic discrimination of measured points that improve the accuracy of final map especially in demanding scenarios such as multi-level maps (e.g., climbing stairs). The parallel programming based nearest neighborhood search implementation such as point to point, point to projection, semantic discrimination of points is used. The 6DSLAM framework is based on modified 3DTK and PCL open source libraries and parallel programming techniques using NVIDIA CUDA. The paper shows experiments that are demonstrating advantages of proposed approach in relation to practical applications. The major added value of presented research is the qualitative and quantitative evaluation based on realistic scenarios including ground truth data obtained by geodetic survey. The research novelty looking from mobile robotics is the evaluation of LS3D algorithm well known in geodesy.

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 paper presents a robust outdoor stereo vision simultaneous localization and mapping (SLAM) algorithm. It estimates camera pose reliably in outdoor environments with directional sunlight illumination causing shadows and non-uniform scene lighting. The algorithm has been developed to measure a mining rope shovel’s rotation angle about its vertical axis (“swing” axis). A stereo camera is mounted externally to the shovel house (upper revolvable portion of the shovel), with a clear view of the shovel’s lower carbody. As the shovel house swings, the camera revolves with the shovel house in a planar circular orbit, seeing differing views of the carbody top. During the swing, the SLAM algorithm builds a map of observed 3D features on the carbody and simultaneously using these landmarks to estimate the camera position. This estimated camera position is then used to compute the shovel swing angle. Two novel techniques are employed to improve the SLAM algorithm’s robustness in outdoor environments. First, a “Locally Maximal” feature selection technique for Harris corners is used to select features more consistently in non-uniformly illuminated scenes. Another novel technique is the use of 3D “Feature Clusters” as SLAM landmarks rather than individual single features. The Feature Cluster landmarks improve the robustness of the landmark matching and allow significant reduction of the SLAM filter computational cost. This approach of estimating the shovel swing angle has a maximum error of ±1° upon SLAM map convergence. Results demonstrate the improvements of using the novel techniques compared to previous methods.

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) techniques are widely researched, since they allow the simultaneous creation of a map and the sensors’ pose estimation in an unknown environment. Visual-based SLAM techniques play a significant role in this field, as they are based on a low-cost and small sensor system, which guarantees those advantages compared to other sensor-based SLAM techniques. The literature presents different approaches and methods to implement visual-based SLAM systems. Among this variety of publications, a beginner in this domain may find problems with identifying and analyzing the main algorithms and selecting the most appropriate one according to his or her project constraints. Therefore, we present the three main visual-based SLAM approaches (visual-only, visual-inertial, and RGB-D SLAM), providing a review of the main algorithms of each approach through diagrams and flowcharts, and highlighting the main advantages and disadvantages of each technique. Furthermore, we propose six criteria that ease the SLAM algorithm’s analysis and consider both the software and hardware levels. In addition, we present some major issues and future directions on visual-SLAM field, and provide a general overview of some of the existing benchmark datasets. This work aims to be the first step for those initiating a SLAM project to have a good perspective of SLAM techniques’ main elements and characteristics.