Pose Estimation for Unknown Motion Target Based on Improved ICP Algorithm and Adaptive EKF

Traditional point cloud registration methods often struggle with noise sensitivity, poor generalization, and limited stability. To address these issues, we propose a multi-stage rigid registration framework combining dynamic oriented bounding boxes (OBB) with an improved Iterative Closest Point (ICP) algorithm. The initial alignment employs curvature-adaptive OBB segmentation and a dual-modal noise filtering approach based on point density and normal vectors. A curvature-weighted coordinate alignment strategy further enhances initial pose estimation. For fine registration, a dynamic weighted ICP algorithm assigns adaptive weights to point pairs using curvature stability and normal alignment error, improving registration precision. Experiments on public datasets (Stanford Bunny315) and industrial oil pan point clouds demonstrate strong performance under varying overlap ratios and noise conditions. Compared with AGICP, the proposed method reduces error by 72% and time by 80% in missing data scenarios, and by 78% and 83%, respectively, in noisy data. For industrial data, it achieves 69% error reduction compared to NDT + ICP and 81% compared to 3D-SIFT + RANSAC, with faster execution. These results show that the proposed method significantly improves accuracy, robustness, and efficiency, making it well suited for practical industrial applications.

In this work, the problem of battery state of charge estimation is investigated using a model based approach. An experimentally validated model of a battery developed by AllCell Technologies, specific for light electric vehicles (electric scooter or bicycles) is used. Two state of charge estimation algorithms are developed: an extended Kalman filter and an adaptive extended Kalman filter. The adaptive version of Kalman filter is designed in order to adaptively set a proper value of the model noise covariance, using the information coming from the on-line innovation analysis. A comparison between the two approaches is conducted that shows that the adaptive Kalman filter can deal with the problem of incorrect value of the model noise covariance matrix producing lower estimation error.

This study presents a data‐driven approach in conjunction with an adaptive extended Kalman filter (AEKF) to estimate lithium‐ion batteries' state of charge (SOC) online. The Thevenin battery model is used to evaluate the effects using battery voltage and current. The advantages of the Lagrange multiplier method are utilized to model the lithium‐ion battery. The Lagrange multiplier method continuously decreases the model error to adjust the Kalman gain of AEKF for accurate SOC estimation. Various current profiles such as hybrid pulse test, dynamic stress test, and Beijing dynamic stress test are used to verify the proposed approach's adaptability, robustness, and accuracy. It is observed that the proposed approach outperforms other methodologies (recursive least square–AEKF and forgetting factor recursive least square–AEKF) due to its high accuracy (mean average error of 0.32%). Additionally, the proposed approach exhibits robustness and high convergence speed despite deliberate erroneous initialization of parameters, thus indicating its applicability in online SOC estimation applications.

The fault location in transmission systems remains a challenging problem, primarily due to the fault location near the substation ends or the weak fault signals. In this study, an adaptive extended Kalman filter (EKF) based on the maximum likelihood (ML) is proposed to estimate the instantaneous amplitudes of the travelling waves. In this method, the EKF algorithm is used to estimate the optimal states (the clean travelling waves) with additive white noise while ML is used to adaptively optimise the error covariance matrices and the initial conditions of the EKF algorithm. Using the proposed method, the singularity points of travelling waves can be detected, and the exact arrival time of the initial wave head at the substations M and N can be easily yielded. Thus the fault distance can be calculated precisely. The effectiveness of exacting mutation feature using the proposed method has been demonstrated by a simulated instantaneous pulse. Also, the proposed method has been tested with different types of faults, such as different fault locations, different fault resistances and different fault inception angles using ATP simulation. The accuracy of fault location using the proposed method has been compared with conventional wavelet transformation scheme.

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A novel method of indoor mobile robot navigation is presented. The proposed approach fuses the data of odometry and electronic compass for navigation. It includes two calibration methods and a fusion algorithm. First of all, calibration method of systematic odometry error is used to reduce the error of navigation and provide the reliable estimate of pose of mobile robot for adaptive extended Kalman filter fusion algorithm later. Secondly, calibration method of electronic compass using an adaptive neural fuzzy inference system provides direction angle of mobile robot as observation for adaptive extended Kalman filter algorithm later. Finally, a fusion algorithm using adaptive extended Kalman filter algorithm fuses data of odometry and electronic compass that provides the position and orientation of mobile robot. In addition, the value of the parameter k of adaptive extended Kalman filter algorithm which is related to the process noise covariance is decided by fuzzy algorithm. In order to illustrate the feasibility of the proposed approach, two types of experiments are done: the first-type experiment is that the mobile robot runs along default path only with odometry, and the mobile robot with odometry and electronic compass in the second-type experiment utilizes the proposed approach for navigation. The results of experiments show that the error of localization and navigation in the first-type experiment is larger than one in the second-type experiment. They prove the good performance of the proposed approach.

Estimation of battery state of charge based on changing window adaptive extended Kalman filtering

Co-estimation for capacity and state of charge for lithium-ion batteries using improved adaptive extended Kalman filter

When 3D laser scanning (LIDAR) is used for navigation of autonomous vehicles operated on unstructured terrain, it is necessary to register the acquired point cloud and accurately perform point cloud reconstruction of the terrain in time. This paper proposes a novel registration method to deal with uneven-density and high-noise of unstructured terrain point clouds. It has two steps of operation, namely initial registration and accurate registration. Multisensor data is firstly used for initial registration. An improved Iterative Closest Point (ICP) algorithm is then deployed for accurate registration. This algorithm extracts key points and builds feature descriptors based on the neighborhood normal vector, point cloud density and curvature. An adaptive threshold is introduced to accelerate iterative convergence. Experimental results are given to show that our two-step registration method can effectively solve the uneven-density and high-noise problem in registration of unstructured terrain point clouds, thereby improving the accuracy of terrain point cloud reconstruction.

In the navigation process, RIMU can work normally, when one of the sensors is broken. When the navigation process finished, in order to reduce costs, only the faulty sensor needs to be changed. Accuracy of RIMU will be reduced, due to the uncalibration sensor. To solve this problem, a self-calibration scheme for RIMU based on adaptive extended Kalman filter (AEKF) is proposed. Firstly, coordinate systems and their relations are given. Then, system model, dynamic model and observation model are derived. Finally, AEKF is proposed to estimate bias, scale errors and align errors. In the proposed scheme, the new sensor can be calibrated accurately without external references. In addition, the proposed scheme shows an excellent adaptability to noise changes.

The stochastic nature of noise signals affects the vehicle’s internal states and the outputs, resulting in the difficulty in estimation. The unknown or time-varying nature of noise signals if not taken into account for estimation, the results will diverge and be highly deteriorated. In this paper, a maximum likelihood principle (MLP) based adaptive robust extended kalman filter for estimating the states of the adopted non-linear vehicle model is proposed. An observability test is done for the purpose of estimation. A covariance matching (CM) based robust adaptive high forgetting factor based fault tolerant technique is also employed on the robust adaptive extended and unscented kalman filters for comparison purpose. The Robustness of the filter is analyzed by varying the parameter of the vehicle through a local sensitivity analysis. The results show that the MLP based approach to the extended kalman filter performs well in three simulations for sinusoidal steering, Double Lance Change, J-Turn, Fishhook, Slalom maneuver in comparison to robust adaptive unscented kalman filter. Friction coefficient of 0.8 (dry road) and 0.4 (wet road) is chosen for the simulation. The sideslip angle RMSE value for MLP based estimation is obtained as 2.62e-05, 4.545e-06 for Sine and DLC maneuver.

Battery management system (BMS) is one of the key subsystems of electric vehicle, and the battery state-of -charge (SOC) is a crucial input for the calculations of energy and power. Therefore, SOC estimation is a significant task for BMS. In this paper, a new method for online estimating SOC is proposed, which combines a novel adaptive extended Kalman filter (AEKF) and a parameter identification algorithm based on adaptive recursive least squares (RLS). Specifically, according to the first order R-C network equivalent circuit model, the battery model parameters are identified online using the RLS with multiple forgetting factors. Based on the identified parameters, the novel AEKF is used to accurately estimate the battery SOC. The online identification of parameter tracks the varying model. At the same time, due to the novel AEKF algorithm to dynamically adjust the system noise parameter, excellent accuracy of the SOC real-time estimation is obtained. Experiments are set to evaluate the accuracy and robustness of the proposed SOC estimation method. The simulation test results indicate that under DST and UDDS conditions, the maximum absolute errors are less than 0.015 after filtering convergence. In addition, the maximum absolute error is less than 0.02 in the simulation of DST with current and voltage measurement noise, so is in DST with current offset sensor error. The tests indicate that the proposed method can accurately estimate battery SOC and has strong robustness.

A robust state-of-charge estimator for multiple types of lithium-ion batteries using adaptive extended Kalman filter

In the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) of non-linear system, the mathematical model and the noise statistics are essential; this limitation was settled by adaptive algorithm. The adaptive extended Kalman filter (AEKF) and adaptive unscented filter (AUKF) were proposed to solve the filtering problem of the system with unknown mathematical model or noise statistics in information fusion. The simulation result shows that the adaptive algorithm in EKF and UKF can improve the performance of filter and decrease the computing time.

Online acquisition of accurate battery electrical, thermal, and aging states is crucial for monitoring and management of batteries, especially in cloud-edge collaboration. In this paper, an online joint estimation method for state of charge (SOC), 3-dimensional state of temperature distribution (SOTD), and state of health (SOH) of batteries based on an electro-thermal-aging coupled model is proposed. Firstly, three well-chosen charging durations are leveraged as the aging features to achieve SOH estimation with the assistance of the extreme learning machine (ELM). Secondly, SOT is estimated through a lumped parameter thermal model (LPTM) and adaptive extended Kalman filter (AEKF), and SOTD is reconstructed using a well-designed fractal theory method (FTM) derived from the iterated function system (IFS) method and a Lindenmayer system (L-system), which ensures precise thermal state monitoring. Then, based on the parameter updating mechanism, the parameters of the equivalent circuit model (ECM) are adjusted in real time according to the accurate SOT and SOH, and the SOC is estimated by the AEKF. Subsequently, the proposed method is validated under different dynamic conditions and different temperatures, the validation results show that the proposed method can achieve accurate SOC-SOTD-SOH co-estimation with an error of no more than 2.14%, 1.37°C, and 1.26%, respectively. Lastly, the investigation of the SOT and SOH influence on SOC estimation reveals the important significance of the co-estimation framework.