Enhanced UAV Indoor Navigation through SLAM-Augmented UWB Localization

Among the existing wireless indoor positioning systems, UWB (ultra-wideband) is one of the most promising solutions. However, the single UWB positioning system is affected by factors such as non-line of sight and multipath, and the navigation accuracy will decrease. In order to make up for the shortcomings of a single UWB positioning system, this paper proposes a scheme based on binocular VO (visual odometer) and UWB sensor fusion. In this paper, the original distance measurement data of UWB and the position information of binocular VO are merged by adaptive Kalman filter, and the structural design of the fusion system and the realization of the fusion algorithm are elaborated. The experimental results show that compared with a single positioning system, the proposed data fusion method can significantly improve the positioning accuracy.

Section: Related Workmentioning

confidence: 99%

UWB/Binocular VO Fusion Algorithm Based on Adaptive Kalman Filter

Zeng

Liu

2019

“…A description of the spatial distributions using methods of quantifying the information, such as the Kullback-Leibler Divergence [43] or the Wasserstein Metric [44], could also yield a more comprehensible assessment of the features. Finally, a combination of the approaches with a dynamic model into a tracking filter or even a simultaneous localization and mapping (SLAM) [45] or semi-supervised learning [46] approach could yield a robust and accurate positioning solution. The evaluation of the proposed approach in a real industrial environment with a variety of tasks implying a semantic map [47] is also of interest.…”

Section: Discussionmentioning

confidence: 99%

UWB Channel Impulse Responses for Positioning in Complex Environments: A Detailed Feature Analysis

Kram

Stahlke

Feigl

et al. 2019

Radio signal-based positioning in environments with complex propagation paths is a challenging task for classical positioning methods. For example, in a typical industrial environment, objects such as machines and workpieces cause reflections, diffractions, and absorptions, which are not taken into account by classical lateration methods and may lead to erroneous positions. Only a few data-driven methods developed in recent years can deal with these irregularities in the propagation paths or use them as additional information for positioning. These methods exploit the channel impulse responses (CIR) that are detected by ultra-wideband radio systems for positioning. These CIRs embed the signal properties of the underlying propagation paths that represent the environment. This article describes a feature-based localization approach that exploits machine-learning to derive characteristic information of the CIR signal for positioning. The approach is complete without highly time-synchronized receiver or arrival times. Various features were investigated based on signal propagation models for complex environments. These features were then assessed qualitatively based on their spatial relationship to objects and their contribution to a more accurate position estimation. Three datasets collected in environments of varying degrees of complexity were analyzed. The evaluation of the experiments showed that a clear relationship between the features and the environment indicates that features in complex propagation environments improve positional accuracy. A quantitative assessment of the features was made based on a hierarchical classification of stratified regions within the environment. Classification accuracies of over 90% could be achieved for region sizes of about 0.1 m 2 . An application-driven evaluation was made to distinguish between different screwing processes on a car door based on CIR measures. While in a static environment, even with a single infrastructure tag, nearly error-free classification could be achieved, the accuracy of changes in the environment decreases rapidly. To adapt to changes in the environment, the models were retrained with a small amount of CIR data. This increased performance considerably. The proposed approach results in highly accurate classification, even with a reduced infrastructure of one or two tags, and is easily adaptable to new environments. In addition, the approach does not require calibration or synchronization of the positioning system or the installation of a reference system.

“…In terms of positioning accuracy, unlike VO technology, it can only provide relative position errors ranging from 0.1% to 2% [3,8]. This method not only solves the problem of absolute positioning of monocular vision, but also achieves the same absolute positioning accuracy as that of literature [19,20]. In the process of indoor positioning, we can accurately determine which side of the wall pedestrians are located, and obtain the environmental information of pedestrians, which plays a good role in emergency rescue in dangerous situations such as nursing homes or elderly people living alone.…”

Section: Methodsmentioning

confidence: 99%

An UWB/Vision Fusion Scheme for Determining Pedestrians’ Indoor Location

Liu

Zhang

Wang

et al. 2020

This paper proposes a method for determining a pedestrian’s indoor location based on an UWB (ultra-wideband) and vison fusion algorithm. Firstly, an UWB localization algorithm based on EKF (extended Kalman filter) is proposed, which can achieve indoor positioning accuracy of 0.3 m. Secondly, a method to solve scale ambiguity and repositioning of the monocular ORB-SLAM (oriented fast and rotated brief-simultaneous localization and mapping) algorithm based on EKF is proposed, which can calculate the ambiguity in real time and can quickly reposition when the vision track fails. Lastly, two experiments were carried out, one in a corridor with sparse texture and the other with the light brightness changing frequently. The results show that the proposed scheme can reliably achieve positioning accuracy on the order of 0.2 m; with the combination of algorithms, the scale ambiguity of monocular ORB-Slam can be solved, with the failed vision trace repositioned by UWB, and the positioning accuracy of UWB can be improved, making it suitable for pedestrian location in indoor environments with sparse texture and frequent light brightness changes.