Abstract
As the indoor dynamic target localization does not detect and repair the circumferential jump value in time, which leads to large position and attitude errors and low‐velocity stability, a combined Global Navigation Satellite System/Simultaneous Localization and Mapping‐ (GNSS/SLAM‐) based high‐precision indoor dynamic target localization method is proposed. The method uses Empirical Mode Decomposition (EMD) to decompose the noisy signal, obtains the noise energy as the dominant mode from the decomposed components, extracts the useful signal energy as the main dividing point, removes the high‐frequency signal, constructs the low‐frequency component to realize low‐pass filtering and denoising, selects a suitable threshold processing function to make the high‐frequency signal component retain the detailed signal effectively to realize high‐frequency component denoising, detects and fixes the circumferential jump of the observed data, and detects and fixes the circumferential jump of each frequency. The indoor dynamic target positioning method is established by combining GNSS/SLAM to achieve high accuracy target positioning. The experimental results show that the position and attitude errors are small, and the velocity is stable, which indicates that the position information is closer to the dynamic target, i.e., the target positioning is more accurate. To address the problems of scale drift and frequent initialization due to environmental factors in monocular vision SLAM, an Ultra Wideband (UWB)/vision fusion localization model that takes into account the scale factor is proposed, which can give full play to the complementary characteristics between UWB and vision; solve the problems of visual initialization, scale ambiguity, and absolute spatial reference; and improve UWB localization accuracy and localization frequency as well as reducing the number of base stations. The model can reliably achieve 0.2 m accuracy in indoor environments with sparse texture or frequent light changes.
Highlights
In recent years, with the growing market for public-facing location services, it has become extremely important to research an efficient, accurate quotient, and low consumption indoor mapping technology, so SLAM technology is gradually becoming a hot topic of research and development in the field of robotics
SLAM problem refers to placing a robot in an unknown environment, where the robot incrementally creates a continuous map of the unknown environment while determining its position in the map
This paper proposes a combined Ultra Wideband (UWB)/vision-based extended Kalman filter (EKF) indoor localization model, which achieves indoor localization accuracy of 0.2 m and solves the problems of visual scale ambiguity and localization failure caused by texture sparsity and light brightness variation
Summary
With the growing market for public-facing location services, it has become extremely important to research an efficient, accurate quotient, and low consumption indoor mapping technology, so SLAM technology is gradually becoming a hot topic of research and development in the field of robotics. As an important research direction in the field of navigation, seamless indooroutdoor positioning technology has received extensive attention and research from governments and scholars [2] At this stage, GNSS-based outdoor high-precision positioning technology is developing rapidly, and indoor positioning technologies such as UWB and INS have made great progress, which can meet the positioning needs in most scenarios [3]. GNSS-based outdoor high-precision positioning technology is developing rapidly, and indoor positioning technologies such as UWB and INS have made great progress, which can meet the positioning needs in most scenarios [3] At this stage, it is still impossible to solve the problem of seamless indoor and outdoor positioning by a single sensor, and the method based on multisensor fusion has become the mainstream technical route [4]. The mapping approach is more costly and difficult to use in GNSS signal occlusion areas, while the SLAM mapping approach is more flexible but less accurate in comparison
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