Unmanned aerial vehicles (UAVs) play a primary role in a plethora of technical and scientific fields owing to their wide range of applications. In particular, the provision of emergency services during the occurrence of a crisis event is a vital application domain where such aerial robots can contribute, sending out valuable assistance to both distressed humans and rescue teams. Bearing in mind that time constraints constitute a crucial parameter in search and rescue (SAR) missions, the punctual and precise detection of humans in peril is of paramount importance. The paper in hand deals with real-time human detection onboard a fully autonomous rescue UAV. Using deep learning techniques, the implemented embedded system was capable of detecting open water swimmers. This allowed the UAV to provide assistance accurately in a fully unsupervised manner, thus enhancing first responder operational capabilities. The novelty of the proposed system is the combination of global navigation satellite system (GNSS) techniques and computer vision algorithms for both precise human detection and rescue apparatus release. Details about hardware configuration as well as the system’s performance evaluation are fully discussed.

Unsupervised Human Detection with an Embedded Vision System on a Fully Autonomous UAV for Search and Rescue Operations

Recent advances and perspectives for positioning and applications with smartphone GNSS observations

An analysis of multi-GNSS observations tracked by recent Android smartphones and smartphone-only relative positioning results

Starting from 2016, the raw Global Navigation Satellite System (GNSS) measurements can be extracted from the Android Nougat (or later) operating systems. Since then, GNSS smartphone positioning has been given much attention. A high number of related publications indicates the importance of the research in this field, as it has been doing in recent years. Due to the cost-effectiveness of the GNSS smartphones, they can be employed in a wide variety of applications such as cadastral surveys, mapping surveying applications, vehicle and pedestrian navigation and etc. However, there are still some challenges regarding the noisy smartphone GNSS observations, the environment effect and smartphone holding modes and the algorithm development part which restrict the users to achieve high-precision smartphone positioning. In this review paper, we overview the research works carried out in this field with a focus on the following aspects: first, to provide a review of fundamental work on raw smartphone observations and quality assessment of GNSS observations from major smart devices including Google Pixel 4, Google Pixel 5, Xiaomi Mi 8 and Samsung Ultra S20 in terms of their signal strengths and carrier-phase continuities, second, to describe the current state of smartphone positioning research field until most recently in 2021 and, last, to summarize major challenges and opportunities in this filed. Finally, the paper is concluded with some remarks as well as future research perspectives.

GNSS smartphones positioning: advances, challenges, opportunities, and future perspectives

The performance of bathymetric measurements by traditional methods (using manned vessels) in ultra-shallow waters, i.e., lakes, rivers, and sea beaches with a depth of less than 1 m, is often difficult or, in many cases, impossible due to problems related to safe vessel maneuvering. For this reason, the use of shallow draft hydrographic Unmanned Surface Vessels (USV) appears to provide a promising alternative method for performing such bathymetric measurements. This article describes the modernisation of a USV to switch from manual to automatic mode, and presents a preliminary study aimed at assessing the suitability of a popular autopilot commonly used in Unmanned Aerial Vehicles (UAV), and a low-cost multi-Global Navigation Satellite System (GNSS) receiver cooperating with it, for performing bathymetric measurements in automated mode, which involves independent movement along a specified route (hydrographic sounding profiles). The cross track error (XTE) variable, i.e., the distance determined between a USV’s position and the sounding profile, measured transversely to the course, was adopted as the measure of automatic control precision. Moreover, the XTE value was statistically assessed in the publication.

https://www.mdpi.com/1424-8220/19/18/3939/pdf

Assessment of the Steering Precision of a Hydrographic Unmanned Surface Vessel (USV) along Sounding Profiles Using a Low-Cost Multi-Global Navigation Satellite System (GNSS) Receiver Supported Autopilot

The Global Navigation Satellite System (GNSS) positioning technology using smartphones can be applied to many aspects of mass life, and the world’s first dual-frequency GNSS smartphone Xiaomi MI 8 represents a new trend in the development of GNSS positioning technology with mobile phones. The main purpose of this work is to explore the best real-time positioning performance that can be achieved on a smartphone without reference stations. By analyzing the GNSS raw measurements, it is found that all the three mobile phones tested have the phenomenon that the differences between pseudorange observations and carrier phase observations are not fixed, thus a PPP (precise point positioning) method is modified accordingly. Using a Xiaomi MI 8 smartphone, the modified real-time PPP positioning strategy which estimates two clock biases of smartphone was applied. The results show that using multi-GNSS systems data can effectively improve positioning performance; the average horizontal and vertical RMS positioning error are 0.81 and 1.65 m respectively (using GPS, BDS, and Galileo data); and the time required for each time period positioning errors in N and E directions to be under 1 m is less than 30s.

/pdf/real-time-precise-point-positioning-with-a-xiaomi-mi-8-3hk4hd1gda.pdf

Real-time Precise Point Positioning with a Xiaomi MI 8 Android Smartphone.

In the era of the Internet of Everything, providing high-precision location services for the public is one of the key directions for the future development of surveying and mapping science. Under this background, Google opened the APIs of Android intelligent terminal to obtain GNSS raw data in 2016. Based on this, this paper mainly explains how to obtain the GNSS raw data of Android smartphone, and uses the Huawei P10 and Xiaomi 8 smartphones installed with self-developed real-time positioning APP to positioning test and analysis accuracy of single-frequency pseudo-range single-point, dual-frequency pseudo-range single point and pseudo-range differential positioning. The problem of inconsistent pseudo-range and carrier phase data in the GNSS raw data of the smartphone is considered to be caused by different clocks of the smartphone; Although there is an inconsistency problem, the positioning accuracy of the smartphone can be improved by the carrier phase smoothing method; In the case of no smoothing, the differential positioning effect is limited due to excessive noise of the pseudo-range; When the carrier phase is smoothed 10–30 times, the pseudo-distance difference method can be used to improve the plane accuracy of Huawei P10 by 10–30%, the plane accuracy of Xiaomi 8 can be increased by 20–40%, and the elevation direction can be increased by 60–70%. Finally, when smoothing 30 epochs for differential positioning, the RMSE in P10 plane positioning is ±3.9 m, the elevation is ±4.3 m, the RMSE in the plane positioning of Xiaomi 8 is ±1.1 m, and the elevation is ±1.4 m. Although the dual-frequency de-ionospheric combination of Xiaomi 8 can effectively eliminate the influence of the ionosphere, it can’t effectively improve the positioning accuracy because it can amplify the noise by 2.59 times, which needs further analysis.

Pseudo-Range Single Point and Differential Positioning Accuracy Test Based on Android Smartphone

The invention discloses a smart-phone-based high-precision single-point positioning method and device. According to the method, the conventional PPP non-combined positioning model is improved and high-precision positioning is performed only by using an original GNSS observation value received by the smart phone without a GNSS reference station. The invention belongs to the technical field of satellite positioning. The positioning method comprises: acquiring observation values, like a pseudo-range and a carrier phase, of an original GNSS of a smart phone; preprocessing data to weaken some error influences and then forming a non-combined model for the original observation values based on an improved accurate single-point positioning method for estimating a double-clock difference; determining weights of all satellite observation values based on a satellite height angle; and carrying out filtering and positioning based on an improved Kalman filtering method to obtain a high-precision single-point positioning result. With the provided positioning method, the positioning accuracy of the smart phone with the plane be superior to 0.9m and the elevation be superior to 1.7m is realized; and the convergence time is less than 30s.

Smart-phone-based high-precision single-point positioning method and device

A high-precision single-point positioning method and apparatus based on a smartphone, wherein same fall within the technical field of satellite positioning. According to the method, a conventional non-combined PPP positioning model is improved, and high-precision positioning is performed only by using an original GNSS observation value received by a smartphone, without the need for a GNSS reference station. The method comprises the following steps: acquiring observation values, such as a pseudo-range and a carrier phase, of an original GNSS of a smartphone; preprocessing data to weaken some error influences and then forming a non-combined model for original observation values according to an improved accurate single-point positioning method for estimating a dual-clock difference; determining weights of satellite observation values according to a satellite height angle; and performing filtering positioning by means of an improved Kalman filtering method to obtain a high-precision single-point positioning result. A test experiment result shows that the positioning precision with the plane being superior to 0. 9 m and the elevation being superior to 1.7 m can be realized for a smartphone by using the method.

Bo Chen

Papers

Real-time Precise Point Positioning with a Xiaomi MI 8 Android Smartphone.

Pseudo-Range Single Point and Differential Positioning Accuracy Test Based on Android Smartphone

Smart-phone-based high-precision single-point positioning method and device

High-precision single-point positioning method and apparatus based on smartphone