The Fine Time Measurement (FTM) protocol introduced by IEEE 802.11 includes a new ranging method, named Wi-Fi Round Trip Time (Wi-Fi RTT), which can be used for indoor localization. Pedestrian Dead Reckoning (PDR) can provide accurate pedestrian tracking through inertial sensors in a short time. Information fusion of PDR and existing wireless technology is widely used in indoor localization to ensure the robustness and stability. In this paper, we propose a fusion indoor localization method of Wi-Fi RTT and PDR. Firstly, an adaptive filtering system consisting of multiple Extended Kalman Filter (EKF) and a new outlier detection method is proposed to reduce the localization error of Wi-Fi RTT. Secondly, the fusion algorithm based on the Federated Filter (FF) and observability is designed to combine Wi-Fi RTT with PDR. Finally, to further improve the localization performance of the fusion algorithm, a real-time smoothing method with fixed interval is used. We evaluate the proposed method in four different scenarios. The results show that the proposed indoor localization method has better stability and robustness, and the average localization error decreased by 37.4-67.6% compared with the classic EKF-based method.

The introduction of data-driven inertial navigation provides new opportunities that the pedestrian dead reckoning could not well provide for constraining inertial system error drift on smartphones, and has been considered as another promising approach to meet the requirement of location-based services. However, indoor localization systems based on a single technology still have their limitations, such as the drift of inertial navigation and the received signal strength fluctuation of Bluetooth, making them unable to provide reliable positioning. To exploit the complementary strengths of each technology, this paper proposes a feasible fusion framework by utilizing a particle filter to integrate data-driven inertial navigation with localization based on Bluetooth Low Energy (BLE). For data-driven inertial navigation, under the premise of using the deep neural network with great potential in model-free generalization to regress pedestrian motion characteristics, we effectively combined the method of using gravity to stabilize inertial measurement units data to make the network more robust. Experimental results show that in the test of different smartphone usages, the proposed data-driven inertial navigation and BLE-based localization technology have good results in modeling user’s movement and positioning respectively. And due to this, the proposed fusion algorithm has almost unaffected by the usages of smartphones. Compared with BLE-based localization that achieved a good mean positional error (MPE) of 1.76m, for the four usages of texting, swinging, calling and pocket, the proposed fusion algorithm reduced the MPE by 32.35%, 20.51%, 20.74%, and 45.37%, respectively, and can further improve localization accuracy on the basis of existing fusion method.

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.

The advent of sensor-rich smart devices (e.g., smartphones) has enabled a lot of applications and services. One of these applications and services is smartphone-based vehicle indoor positioning, which is a key technology for smart car parking and driverless cars. So far, most vehicle indoor positioning solutions either use infrastructures (e.g., WiFi access points) or inertial sensors, which suffer from low positioning accuracy, limited coverage, or high cost to deploy new equipment. To tackle these challenges, in this work we propose a novel Deep Learning-based Vehicle Indoor Positioning (DeepVIP) approach using smartphone built-in sensors, including accelerometer, gyroscope, magnetometer, and gravity sensor. Experiments are conducted in indoor parking areas. Experimental results show that the proposed method outperforms the state-of-the-art methods.

Global geological conditions are complex and variable, characterized by frequent plate movements, earthquakes, and volcanic eruptions. Coupled with significant climate differences, various factors interact to trigger frequent landslide disasters, resulting in substantial losses of life and property. Therefore, landslide monitoring is crucial. Traditional monitoring technologies face limitations when dealing with complex terrains and meeting the demands for high timeliness, while unmanned aerial vehicles (UAVs), with their maneuverability, high resolution, and ability to operate in hazardous environments, have been widely applied in landslide monitoring. This paper provides a comprehensive review of UAV-based 3D reconstruction for landslides, detailing the characteristics and application cases of UAVs, explaining the functions and limitations of sensors such as optical sensors and light detection and ranging (LiDAR), and exploring 3D reconstruction methods based on UAV imagery, LiDAR, and hybrid approaches. It analyzes the applications of UAV 3D reconstruction in landslide emergency investigation, monitoring, and disaster assessment. The paper identifies the technical challenges faced in these applications and proposes corresponding solutions. In addition, UAV-based 3D reconstruction technology—with its centimeter-level spatial resolution—enables the precise delineation of landslide extent and hazard potential, thereby enhancing monitoring accuracy and improving the efficiency of emergency investigations. This technology provides strong technical support for landslide research and prevention, with significant implications for reducing landslide disaster losses.

Accurate image matching is a crucial prerequisite for multimodal data fusion in Earth observation and serves as a foundational technology for deep-space exploration. While Earth multimodal remote sensing images (EMRSIs) often suffer from severe nonlinear radiometric differences (NRD) and geometric deformations, planetary multimodal remote sensing images (PMRSIs) introduce additional challenges due to disparate sensing modalities and predominantly weak-texture surfaces. To address these cross-domain challenges, we propose a Modality-Invariant Structural Representation (MISR) framework designed for robust matching of both EMRSIs and PMRSIs. The MISR framework consists of two core modules. First, a novel image scale space is constructed using Weighted Side-window based Gradient Guided Filtering (WSGGF), which facilitates effective noise suppression while preserving essential structural and edge features. Second, a multi-order structural representation is introduced via the Multi-order Gradient Location and Orientation Histogram (MGLOH) descriptor, which integrates a weighted dominant orientation assignment with an enhanced spatial configuration. By fusing first- and second-order gradient responses, MGLOH offers improved rotation invariance and stronger feature discrimination. Extensive experiments conducted on both Earth and planetary datasets demonstrate that the proposed method achieves superior matching accuracy and robustness compared to state-of-the-art approaches.