Scan-to-map matching using the Hausdorff distance for robust mobile robot localization

Knowing the position of a mobile robot in the environment in which it operates is an important element for effectively accomplishing complex tasks requiring autonomous navigation. Among several existing techniques for robot self-localization, a new approach called Hough localization was proposed for map matching in the Hough domain, that turned out to be reliable and efficient for position tracking in polygonal environments. In this paper we present an extension of Hough localization which is able to deal with the global localization problem, in which the robot does not know its initial position in the environment.

The architecture of a Gaussian mixture Bayes (GMB) robot position estimation system

Many procedures in orthopedic surgery rely on navigation to accurately place instrumentation. These methods include fluoroscopic, stereotactic and robotic approaches. While contemporary systems have been successful at dramatically lowering reoperation rates, they may not always be available due to their significant costs ($850,000-$1,200,000) or appropriate due to their ionizing radiation. There is an interest in developing lower-cost, radiation-free, and accurate navigation. We propose to address this issue using ultrasound measured distances to nearby bone surfaces. Distances can be processed by a state-estimation algorithm to determine the position and pose of the probe relative to a preoperative CT scan. Recent studies have shown that ultrasound can accurately identify bone surfaces, and algorithms based on range measurements are used for accurate localization in mobile robotics. This study evaluates the feasibility of combining these techniques to make sufficient position and pose measurements in an anatomically realistic model. We assessed position and pose estimation accuracy in a simplified 2D space using a linear 2D ultrasound (L-14W/60, Ultrasonix Corp., Canada) to image (1) an adult L4 vertebra model (∼80 mm across), and (2) a set of ‘V’ shapes characterized by their internal angle. The probe was immersed in a water bath at ten different positions and orientations and images were acquired. The US probe position was measured using an NDI Vega optical tracker (Northern Digital Inc., Canada). The images were manually segmented and distance measurements to the model surface computed. We used a multistart interior point optimization algorithm to compute a position and pose that minimized an objective function based on the average squared distances between the predicted and measured distances to the model surface. We then computed the errors relative to the position reported by the optical tracker. The mean localization error of the probe around the anatomically-realistic model was 0.7mm in translation and 2° in rotation. The algorithm obtained similar errors within a range of initial position estimates of up to 12 mm and 20° from the true position. Errors in the parametric study decreased from 1.65 mm and 3.8° for a ‘V’ angle of 90° to 0.3 mm and 0.5° for a ‘V’ angle of 150°. These accuracies suggest that the proposed technique is sufficiently accurate to justify further development. Limitations include the use of a conventional 2D US probe, the 2D plane scenario rather than the real 3D use case, and the use of manual bone segmentation as opposed to an automated algorithm. Future work is planned to address these limitations.

The Permanent Magnet-Assissted Synchronous Reluctance Motor (PMA-SynRM) drive has become one of the most interesting replacements for the high efficiency variable speed drive. Herein, sensorless predictive torque control of a PMA-SynRM with non-sinusoidal back electromotive force (Back-EMF) is introduced. In order to control PMA-SynRM, finite control set-model predictive control (FCS-MPC) is implemented by means of a two-level inverter. Furthermore, an improved form of FCS-MPC, i.e., direct mean torque control (DMTC), is utilized as a second method to control PMA-SynRM. For improving the sensorless the combination of Extended Kalman Filter (EKF), Adaptive Filter (AF) and quadrature Phase-Locked Loop (PLL) are used for better estimation of the non-sinusoidal back EMF, elimination of the high order harmonics, and the accurate estimation of position and speed rotor, respectively. The simulation result in effectively minimizing torque ripples compared to conventional FCS-MPC. The outcomes of the observer simulation are successfully guaranteed the accurate estimation of speed and rotor position.

Recently, various autonomous mobile robots have been developed for practical use. To support the coexistence of robots and humans in real environments, we propose a concept named ‘Region with Velocity Constraints (RVC),’ which is set around hazardous areas. RVCs are regions where the velocities of the robot are constrained to predefined values. Inside the RVCs, the robot has to reduce its translational velocity to avoid predicted hazards such as collisions with obstacles, and to reduce its rotational velocity to prevent undesirable motions such as sharp turns. We also propose a motion planning method for navigating the mobile robot in an environment with RVCs based on the Navigation Function and Global Dynamic Window Approach. Our method generates a trajectory satisfying both translational and rotational velocity constraints to be compatible with the surroundings. Moreover, to demonstrate the validity of our method, we performed numerical simulations and experiments.

Motivation: Motion during MRI can severely impact image quality, necessitating advanced techniques to accurately sense and compensate for head movements. Goal(s): This study aims to develop a novel algorithm for high-fidelity motion sensing and position estimation for 3D Radial head scan. Approach: By using Region-Optimized Virtual Coils to move the coil profile and Fourier Slice Theorem to calculate center-of-mass from few spokes, the algorithm detects positional changes and estimates rigid-body motion parameters in high-temporally. Results: Experimental validation using simulated and real datasets demonstrated effective motion detection and significant improvements in image quality, showcasing the method's robustness in motion correction. Impact: This research offers a groundbreaking approach to high-temporal motion sensing in MRI, enhancing image quality through accurate position estimation. It opens avenues for future investigations into motion compensation techniques, ultimately benefiting clinical applications in brain imaging and diagnostics.

In the permanent magnet synchronous motor (PMSM) sensorless drive method, motor inductance is a decisive parameter for rotor position estimation. Due to core magnetic saturation, the motor current easily invokes inductance variation and degrades rotor position estimation accuracy. For a constant load torque, saturated inductance and inductance error in the sensorless drive method are constant. Inductance error results in constant rotor position estimation error and minor degradations, such as less optimal torque current, but no speed estimation error. For a periodic load torque, the inductance parameter error periodically fluctuates and, as a result, the position estimation error and speed error also periodically fluctuate. Periodic speed error makes speed regulation and load torque compensation especially difficult. This paper presents an inductance parameter estimator based on polynomial neural network (PNN) machine learning for PMSM sensorless drive with a period load torque compensator. By applying an inductance estimator, we also proposed a magnetic saturation compensation method to minimize periodic speed fluctuation. Simulation and experiments were conducted to validate the proposed method by confirming improved position and speed estimation accuracy and reduced system vibration against periodic load torque.

Mobile robot localization and map building are very important for a mobile robot to perform a navigation-based service task to aid the aged or disabled in an indoor environment (office, facilities or at home). In order to enable mobile robot to perform service tasks autonomously, the information of environment in which the mobile robot moves is needed. To obtain the accurate map, many different sensors system and techniques have been developed. Approaches of mapping can roughly be classified according to the kind of sensor data processed and the matching algorithms [S. Thrun et al., 1996, 1998, 2000, 2004; Chong, K.S et al., 1996; C.-C. Wang et al., 2002; G. Dissanayake et al., 2000; B. barshan et al., 1995; Weimin Shen et al., 2005]. In this paper, we proposed a method of map building using interactive GUI for a mobile robot. The reason we proposed this method is that it is difficult for a mobile robot to generate an accurate map although many kinds of sensors are used. The proposed method enables the operator to modify map built by sensors, compared with the real-time video from web camera by using modification tool in interactive GUI. Laser Range Finder (LRF) was used to get the information of the environment in which mobile robot moving. In order to improve self-localization of mobile robot, Extended Kalman Filter (EKF) was introduced. By the results of simulation and experiments, the developed EKF is effective in self-localization of mobile robot. This paper introduces the architecture of the system and gives some experimental results to verify the effectiveness of the developed system. The rest of the paper consists of 5 sections. Section 2 describes the structure of the proposed system. Section 3 presents the developed algorithm of self-localization of mobile robot and gives some simulation and experimental results to verify the effectiveness of the EKF in improving precision of positioning of mobile robot. Section 4 details the developed LRF data processing algorithm. Section 5 introduces the developed interactive GUI. The experimental results are given in Section 6. Section 7 concludes the paper. 13

This paper proposes an online sensorless rotor position estimation technique for switched reluctance motors, by which the real-time rotor position is estimated by detecting the virtual inductance vector of the machine. With the proposed technique, rotor position can be estimated accurately, even if the inductance of each phase is asymmetrical. It is achieved by first detecting real-time voltage and real-time current of each phase winding, according to which flux-linkage and inductance of each phase can be thus calculated. With the calculated inductance of each phase, a coordinate system can be established for rotor position estimation. However, since the position estimation accuracy will be reduced by asymmetrical phase inductance, a conversion rule is proposed to convert actual phase inductance to virtual inductance, by which the symmetry of phase inductance can be corrected. Then, according to virtual inductance, a new coordinate system is established for rotor position estimation. In the new coordinate system, position estimation shows high accuracy under both normal condition and asymmetric inductance condition. To conclude, simulation and experimental results are given to verify the effectiveness of the proposed sensorless position estimation technique.

One of the problems faced by soccer robots is how to find out the position of the robot itself and other robots on the field. A simple way to find out the robot's position is to use the odometry method. However, odometry is weak in accumulating position errors that reduce the accuracy of the moving robot's absolute position estimation and orientation. This paper presents a robot position data estimation system that is to be implemented on the ERSOW wheeled soccer robot. The robot can determine its position based on a unique landmark: an L-shaped line on the soccer robot field. We use a deep neural network method to recognize landmark L-shaped. Vision systems and deep learning inferences run on the Robot Operating System platform. After obtaining the distance of the robot to the L-shaped landmark, the robot's orientation and position relative to the field can be accurately determined based on the omnidirectional camera's perception. The results of the position estimation system in this study can be used to reduce position errors resulting from the odometry method. Based on the landmark L-shape recognition test results conducted on 641 datasets, the validation accuracy value is 0.806. The results of testing the robot position generated by vision obtained the largest error x about 2.32 cm and y about 1.99 cm.

In this paper, a novel genetic algorithm based approach to path planning of a mobile robot is proposed. The major characteristic of the proposed algorithm is that the chromosome has a variable length. The location target and obstacles are included to find a path for a mobile robot in an environment that is a 2D workplace discretized into a grid net. Each cell in the net is a gene. The number of genes in one chromosome depends on the environment. The locations of the robot, the target and the obstacle are marked in the workplace. The proposed algorithm is capable of generating collision-free paths for a mobile robot in both static and dynamic environments. In a static environment, the generated robot path is optimal in the sense of the shortest distance. The effectiveness of the proposed model is demonstrated by simulation studies.

By using high resolution range information from multiple radar stations in monostatic or bistatic modes, the position estimates can be highly improved compared with conventional measurements from a single radar. In this paper, fundamental geometry relationships and the possible accuracy of target position and velocity estimates are analyzed when data from several radars are combined. The velocity error is proportional to the wavelength and the Doppler resolution, which is limited by the available time of measurement. The estimation error increases when the radars are close, or when the target is close to the radar level surface. For ground stations, the accuracy in estimated target altitude is usually significantly lower than the position data in the horizontal plane. Examples and simulation results are presented, displaying the performance of three radars working in bistatic and monostatic modes. The analysis is generalized to an arbitrary number of radar stations.

Position estimation based on scan matching is a type of popular approaches for mobile robot localization in indoor office-like environments. Global scan matching and local scan matching are two basic position estimation problems. Global scan matching is subject to global localization for mobile robot and local scan matching is used to deal with tracking mobile robot position over time. Global localization based on scan matching is represented in this paper. A global map is built to represent whole environment feature and a local map is built to represent current range scan. Global map and local map consist of a lot of line segments. Complete line segment that represents a complete environment object is singled out and used to match between local map and global map. Line segments are sorted counterclockwise in two maps. This type of ordinal map improves the searching efficiency of matching and localization algorithm is timesaving. In addition, matching between current range scan and global map is based on relative position relationships of line segments in local and global map. Global localization based on relative position relationship avoids frequent coordinates transformation and lowers computational cost. All these techniques have been implemented on our mobile robot ATRVII equipped with 2D laser range scanner SICK.

Localization is an important topic in mobile robots. It is essential that a mobile robot plans movement and reaches goals. In this paper, we described self-localization technique for mobile robot based on particle filtering in active beacon system. The basic method is to estimate value of position and heading of mobile robot using ultrasonic sensor as particle filter is used to eliminate process and measurement noise. Several variants of the particle filter such as SIR and RPF are introduced. These are discussed and compared with EKF for localization of mobile robot.

This paper presents a novel adaptive iterated extended Kalman filter (AIEKF) for relative position and attitude estimation, taking into account the influence of model uncertainty. Considering a nonlinear stochastic discrete‐time system with unknown disturbance, the AIEKF algorithm adopts the Gauss‐Newton iterative optimization steps to implement a maximum a posteriori (MAP) estimation, and the switch‐mode combination technique is used to achieve the adaptive capability. The mean‐square estimation error (MSE) of the state estimate is derived. It is proved that the AIEKF can yield a smaller MSE than that of the traditional extended Kalman filter (EKF) or iterated extended Kalman filter (IEKF). The performance advantage of the AIEKF is illustrated via Monte Carlo simulations on a typical relative position and attitude estimation application. Through comparisons in different scenarios, the presented algorithm is shown to improve adaptability and ensure estimation accuracy.