Range-based SLAM is a well-established technique for estimating the pose of a mobile robot operating in an unknown environment. Current state-of-the-art solutions use occupancy-grid maps to represent the world. While fast and accurate, their performance is limited by two facts. First, in an occupancy-grid map measurements have to be discretised into cell resolution. Second, online pose estimation, which relies on scan-to-map alignment, typically requires smoothing/interpolating the discrete grid-map. This letter presents a SLAM technique that builds on top of a B-spline surface map. The local properties of splines and the inherent smoothness of their basis function handle the aforementioned problems, without significant increase in the computational cost. Through qualitative and quantitative tests using public data sets we show that the proposed B-spline SLAM is an affordable technique that delivers accurate results at sensor rate speed.

Online Range-Based SLAM Using B-Spline Surfaces

In this paper, a new single-output resistive sensor is proposed to enhance the dynamics estimation, slip elimination, stability extraction and surface scanning of a ground UGV (wheeled-robot) in 2D and 3D spaces. The new sensor is based on the total resistance of a circuit which contains a continuous resistive belt with high accuracy. The Lagrange method is implemented to derive the stick-slip dynamics in both 2D and 3D spaces. Furthermore, the kinematics is solved using the Newton-Raphson approach. The slipping characteristics of the robot with and without the new sensor are firstly shown in 2D space. To demonstrate the abilities of the sensor in the real applications, the dynamic simulation is further extended to 3D space. A real time torque optimization aided by the new sensor is applied to the dynamics of the robot to eliminate slip during locomotion. Moreover, a stability measure is introduced and the real time stability margins are extracted during missions. The sensor empowers the robot to scan the surface and consequently, extract the main properties of the surface. The simulation results obtained for different case studies prove the ability of the new sensor in performing the above mentioned tasks.

A New Contact Angle Detection Method for Dynamics Estimation of a UGV Subject to Slipping in Rough-Terrain

In this paper, a new, simple and cheap sensor is proposed to detect the multipoint contact of a typical robotic wheel. The new sensor empowers a wheeled robot to scan the surface and to find stability margins during real-time locomotion without camera or laser sensors. Furthermore, it enhances the ability of the real-time solution of the dynamic equations. The new sensor is based on the total resistance of a circuit and can be classified into two types. In the first type, the resistive circuit includes a conductive path, a direct voltage source and some resistors by various values. The main advantage of this type of the new sensor is to determine the exact locations of multipoint contact only by means of an input data detecting the voltage of a resistor. The implementation of the new idea is easy to use and to be experimented. It can be used to improve the control process, especially on the rough surfaces and to enhance the locomotion stability. The second type of the new sensor contains a continuous resistive belt with higher accuracy than the first type. The algorithm of the multipoint contact detection is explained and the kinematics relations of the robot are obtained. The surface is scanned during the robot locomotion, and the error of the estimated surface profile is calculated. Finally, the static stability margins are extracted using the new sensor data.

A New Resistive Belt Sensor for Multipoint Contact Detection of Robotic Wheels

Because 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.,Submaps 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.,Simulation 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.,The proposed method is robust to sparse pedestrians and can be adapted to most indoor environments.,In this paper, a two-level optimized graph-based SLAM algorithm is proposed.

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

Purpose: Localization and mapping with LiDAR data is a fundamental building block for autonomous vehicles. Though LiDAR point clouds can often encode the scene depth more accurate and steadier compared with visual information, laser-based Simultaneous Localization And Mapping (SLAM) remains challenging as the data is usually sparse, density variable and less discriminative. The purpose of this paper is to propose an accurate and reliable laser-based SLAM solution. Design/methodology/approach: The method starts with constructing voxel grids based on the 3D input point cloud. These voxels are then classified into three types to indicate different physical objects according to the spatial distribution of the points contained in each voxel. During the mapping process, a global environment model with Partition of Unity (POU) implicit surface is maintained and the voxels are merged into the model from stage to stage, which is implemented by Levenberg–Marquardt algorithm. Findings: We propose a laser-based SLAM method. The method uses POU implicit surface representation to build the model and is evaluated on the KITTI odometry benchmark without loop closure. Our method achieves around 30% translational estimation precision improvement with acceptable sacrifice of efficiency compared to LOAM. Overall, our method uses a more complex and accurate surface representation than LOAM to increase the mapping accuracy at the expense of computational efficiency. Experimental results indicate that the method achieves accuracy comparable to the state-of-the-art methods. Originality/value: We propose a novel, low-drift SLAM method that falls into a scan-to-model matching paradigm. The method, which operates on point clouds obtained from Velodyne HDL64, is of value to researchers developing SLAM systems for autonomous vehicles.

/pdf/pou-slam-scan-to-model-matching-based-on-3d-voxels-2kkxo1bsmb.pdf

POU-SLAM: Scan-to-Model Matching Based on 3D Voxels

Purpose




The purpose of this paper is to present a scan matching simultaneous localization and mapping (SLAM) algorithm based on particle filter to generate the grid map online. It mainly focuses on reducing the memory consumption and alleviating the loop closure problem.




Design/methodology/approach




The proposed method alleviates the loop closure problem by improving the accuracy of the robot’s pose. First, two improvements were applied to enhance the accuracy of the hill climbing scan matching. Second, a particle filter was used to maintain the diversity of the robot’s pose and then to supply potential seeds to the hill climbing scan matching to ensure that the best match point was the global optimum. The proposed method reduces the memory consumption by maintaining only a single grid map.




Findings




Simulation and experimental results have proved that this method can build a consistent map of a complex environment. Meanwhile, it reduced the memory consumption and alleviates the loop closure problem.




Originality/value




In this paper, a new SLAM algorithm has been proposed. It can reduce the memory consumption and alleviate the loop closure problem without lowering the accuracy of the generated grid map.

A scan matching simultaneous localization and mapping algorithm based on particle filter

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A scan matching simultaneous localization and mapping algorithm based on particle filter

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