Common Localization Methods Research Articles

Extended Kalman Filter State Estimation for Autonomous Competition Robots

Autonomous mobile robot competitions judge based on a robot’s ability to quickly and accurately navigate the game field. This means accurate localization is crucial for creating an autonomous competition robot. Two common localization methods are odometry and computer vision landmark detection. Odometry provides frequent velocity measurements, while landmark detection provides infrequent position measurements. The state can also be predicted with a physics model. These three types of localization can be “fused” to create a more accurate state estimate using an Extended Kalman Filter (EKF). The EKF is a nonlinear full-state estimator that approximates the state estimate with the lowest covariance error when given the sensor measurements, the model prediction, and their variances. In this paper, we demonstrate the effectiveness of the EKF by implementing it on a 4-wheel mecanum-drive robot simulation. The position and velocity accuracy of fusing together various combinations of these three data sources are compared. We also discuss the assumptions and limitations of an EKF.

Extended Kalman Filter State Estimation for Autonomous Competition Robots

Autonomous mobile robot competitions judge based on a robot’s ability to quickly and accurately navigate the game field. This means accurate localization is crucial for creating an autonomous competition. Two common localization methods are odometry and computer vision landmark detection. Odometry provides frequent velocity measurements, while landmark detection provides infrequent position measurements. The state can also be predicted with a physics model. These three types of localization can be “fused” to create a more accurate state estimate using an Extended Kalman Filter (EKF). The EKF is a nonlinear full-state estimator that approximates the state estimate with the lowest covariance error when given the sensor measurements, the model prediction, and their variances. In this paper, we demonstrate the effectiveness of the EKF by implementing it on a 4-wheel mecanum-drive robot simulation. The position and velocity accuracy of fusing together various combinations of these three data sources are compared. We also discuss the assumptions and limitations of an EKF.

A Comparative Study of Localization Methods in EnkF Data Assimilation

This study investigates the relation between some common localization methods in ensemble Kalman filter (EnKF) systems including covariance localization (CL) and local analysis (LA), which are popular used in large-scale Geo-science applications. Two fuzzy-based localization methods, named covariance fuzzy (CF) and fuzzy analysis (FA), are formulated in terms of tapering of ensemble covariance in a fuzzy logic way. To explore the effects of all algorithms on the error covariance matrix, numerical experiments are designed using a classical nonlinear model (i.e., the Lorenz-96 model) by determining the Power Spectrum Density (PSD) of the corresponding systems. The experiments show that the new algorithms can eliminate spurious correlation of the background error covariance matrix. The results of PSD demonstrate that the new fuzzy based methods have more robust performance than the CL or LA algorithm.

Localization of emergency acoustic sources by micro aerial vehicles

AbstractFor micro aerial vehicles (MAVs) involved in search and rescue missions, the ability to locate the source of a distress sound signal is significantly important and allows fast localization of victims and rescuers during nighttime, through foliage and in dust, fog, and smoke. Most emergency sound sources, such as safety whistles and personal alarms, generate a narrowband signal that is difficult to localize by human listeners or with the common localization methods suitable for broadband sounds. In this paper, we present three methods for MAV‐based emergency sound localization system. The first method involves designing a new emergency source for immediate localization by the MAV using a common localization method. The other two novel methods allow localizing the currently available emergency sources, or other narrowband sounds in general, that are difficult to localize due to the periodicity in the sequence of sound samples. The second method exploits the Doppler shift in the sound frequency, caused due to the motion of the MAV and the dynamics of the MAV to assist with the localization. The third method involves active control of the robot's attitude and fusing acoustic and attitude measurements for achieving accurate and robust estimates. We evaluate our methods in real‐world experiments with real flying robots.

Suitability of revision to MUSLE for estimating sediment yield in the Loess Plateau of China

This study evaluated the effect of a combination of revisions to the cover and management factor (C), support practice factor (P), and topographic factor (LS), and to determine the best use of each combination for modified universal soil loss equation (MUSLE) being applied in the Loess Plateau of China. Eight combinations were built based on common local methods of revision to factors C, P, and LS in three ways (single factor revised, two factors revised, and three factors revised, respectively). By validating the effect of these combinations on sediment yield estimation using the Nash–Sutcliffe coefficient of efficiency (NSE), normalized root-mean-square error (NRMSE), correlation coefficient (R), and angle between liner trend line and 1:1 line, the optimal combination was selected. Results show that: (1) the original method significantly overestimates the sediment yield; (2) when revising a single factor, the LS factor revision has the greatest positive effect; (3) when revising two factors, the combination of a simultaneous revision to C and to P has the smallest angle between the liner trend line and the 1:1 line (1.37°); if the estimating accuracy of sediment peak is required, this combination may thus be a useful approach; and (4) when revising C, P, and LS simultaneously, the combination has the highest NSE (0.76), lowest NRMSE (1.19), and the highest correlation coefficient (0.760), and may be used to estimate sediment yield. The suitability of MUSLE is enhanced using the common local revision to factors C, P, and LS. Under the assumption that level terrace spatial information can obtain effectively (or that there are few level terraces), these findings can be applied to the Loess Plateau for the design of soil and water conservation measures and for flood disaster assessment, and applied to future model applications in the Loess Plateau.

The effect of anesthesia type on needle localization breast biopsy: Another point of view

Efforts directed at early detection of breast cancer have resulted in an increased incidence of nonpalpable mammographic lesions that warrant excisional biopsy. The most common localization method is by use of the hook wire technique, or needle localization biopsy. Although much has been written about the localization technique, the impact of the method of anesthesia on the accuracy of the biopsy and especially on the completeness of the excision has not been clarified. We studied 450 needle localization breast biopsies to determine whether the type of anesthesia (local versus general) influenced the accuracy and completeness of the biopsy. We compared 153 biopsies performed under local anesthesia to 297 done under general anesthesia. The use of local versus general anesthesia did not affect accuracy; however, it did determine the inability to achieve clean margins (27.6% versus 7.3%, respectively, P <0.02). It was more difficult to excise completely specimens located deeper than 3 cm in the breast, when the localizing needle travelled more than 3 cm, and when the lesions were of the microcalcification mammographic pattern. For lesions mammographically suspicious for malignancy, mainly those located deeper than 3 cm, general anesthesia is preferred.