To address the ill-posedness of the Hessian matrix in monocular visual-inertial SLAM (Simultaneous Localization and Mapping) caused by unobservable depth of feature points, which leads to convergence difficulties and reduced robustness, this paper proposes a Pre-Dog-Leg feature optimization method based on an adaptive preconditioner. First, we propose a multi-candidate initialization method with robust characteristics. This method effectively circumvents erroneous depth initialization by introducing multiple depth assumptions and geometric consistency constraints. Second, we address the pathology of the Hessian matrix of the feature points by constructing a hybrid SPAI-Jacobi adaptive preconditioner. This preconditioner is capable of identifying matrix pathology and dynamically enabling preconditioning as a strategy. Finally, we construct a hybrid adaptive preconditioner for the traditional Dog-Leg numerical optimization method. To address the issue of degraded convergence performance when solving pathological problems, we map the pathological optimization problem from the original parameter space to a well-conditioned preconditioned space. The optimization equivalence is maintained by variable recovery. The experiments on the EuRoC dataset show that the method reduces the number of Hessian matrix conditionals by a factor of 7.9, effectively suppresses outliers, and significantly improves the overall convergence time. From the analysis of trajectory error, the absolute trajectory error is reduced by up to 16.48% relative to RVIO2 on the MH_01 sequence, 20.83% relative to VINS-mono on the MH_02 sequence, and up to 14.73% relative to VINS-mono and 34.0% relative to OpenVINS on the highly dynamic MH_05 sequence, indicating that the algorithm achieves higher localization accuracy and stronger system robustness.

Permanent magnet synchronous motor (PMSM) drives commonly use a mechanical position sensor, and position sensorless control removes this transducer by reconstructing rotor position and speed from electrical measurements. Fractional order operators have been introduced into observers, speed adaptation laws, position reconstruction loops, disturbance compensators, and feedback controllers to shape the estimation and regulation dynamics. This paper combines the operating characteristics of position sensorless PMSM drives and summarizes the application of fractional order methods in this field, including fractional order sliding mode observation, model reference adaptation, flux and back EMF estimation, and position reconstruction based on phase locked and frequency locked loops, as well as the interaction between fractional order controllers and estimated rotor feedback and the digital realization of non-integer order operators. The reviewed literature shows improvements in selected position error, speed response, and harmonic measures under specified machines, operating points, and baselines; however, component-level convergence does not generally establish stability of the complete output-feedback drive, back EMF methods retain an information limitation near standstill, and simultaneous changes to observers, controllers, and tuning procedures impede attribution to the fractional order element. Based on the current status, the development prospects for fractional order sensorless control are discussed, and the conditions under which a fractional-order design can be assessed reproducibly are identified.

Traditional land vehicle gravity measurement heavily rely on high-precision satellite navigation positioning information. However, the operational range of satellite navigation is limited, and it cannot maintain the required level of accuracy in special environments. To address this issue, we propose a novel land vehicle gravity anomaly measurement method based on altimeter-assisted strapdown inertial navigation system (SINS)/dead reckoning (DR) integration. Gravimetric measurement trials demonstrate that after low-pass filtering, the new method achieves a fit accuracy of 2.005 mGal, comparable to that of the traditional SINS/global navigation satellite system (GNSS) integration method. Compared with the SINS/DR integration method, the proposed method improves accuracy by approximately 11%.

Land vehicle gravity anomaly measurement relies heavily on global navigation satellite system (GNSS). However, when gravity measurement is carried out in special environments such as forests, valleys and tunnels, GNSS observation quality will inevitably decline, which directly affects the accuracy of gravity anomaly measurement. From the point of view of the gravity anomaly measurement principle, obtaining accurate elevation information of the test line is the premise to ensure the accuracy of gravity anomaly measurement. Thus, this paper proposes a strapdown land vehicle dynamic gravity anomaly measurement method combining an odometer and a geo-information system. In this method, strapdown inertial navigation errors are suppressed by observing the velocity of the odometer output. Then, the position information obtained by the combined navigation is entered into the geo-information system to obtain the elevation. The results of a single test line show that the external coincidence accuracy of the proposed method is 1.65 mGal, and the accuracy is comparable to the traditional GNSS assisted land vehicle gravimetry method. In addition, compared with the odometer assisted land vehicle gravimetry method, the external coincidence accuracy is increased by 30%.

Event cameras and low-cost microelectromechanical systems (MEMS) inertial measurement units (IMUs) are widely used in event-based visual–inertial odometry (EVIO). However, the bias instability and noise of MEMS gyroscopes accelerate drift accumulation, making loop-closure detection essential for global consistency. Existing EVIO systems largely inherit loop-detection pipelines from conventional visual–inertial odometry, whose fixed score thresholds and raw inlier counts degrade when event-frame quality fluctuates during high-speed motion or under high-dynamic-range illumination. This paper proposes MEVFusion-LD, which fuses MEMS inertial and event-vision measurements using adaptive scoring and match-quality weighting. First, an adaptive scoring threshold (AST) mechanism updates the candidate-entry threshold online from the first- and second-order statistics of query scores, with bilateral clamping. Second, temporal-consistency voting accumulates retrieval votes within a sliding cache window to suppress sporadic false candidates. Third, match-quality-weighted verification (MQWV) replaces the raw inlier count with a quality-weighted inlier ratio for geometric confirmation, followed by 4-DoF pose-graph optimization for online drift correction. Experiments on the VECtor and MVSEC datasets, both collected using MEMS-grade inertial sensors, show that MEVFusion-LD reduces the mean absolute trajectory error by 29.8%, 41.1%, and 50.0% relative to ESVIO, ESIO, and PL-EVIO, respectively, achieves the best accuracy on 9 of 11 sequences, and reduces ATE by 52.8% on average through the loop module alone.