We present the development of a low-cost Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) system and an accompanying workflow to produce 3D point clouds. UAV systems provide an unrivalled combination of high temporal and spatial resolution datasets. The TerraLuma UAV-LiDAR system has been developed to take advantage of these properties and in doing so overcome some of the current limitations of the use of this technology within the forestry industry. A modified processing workflow including a novel trajectory determination algorithm fusing observations from a GPS receiver, an Inertial Measurement Unit (IMU) and a High Definition (HD) video camera is presented. The advantages of this workflow are demonstrated using a rigorous assessment of the spatial accuracy of the final point clouds. It is shown that due to the inclusion of video the horizontal accuracy of the final point cloud improves from 0.61 m to 0.34 m (RMS error assessed against ground control). The effect of the very high density point clouds (up to 62 points per m2) produced by the UAV-LiDAR system on the measurement of tree location, height and crown width are also assessed by performing repeat surveys over individual isolated trees. The standard deviation of tree height is shown to reduce from 0.26 m, when using data with a density of 8 points perm2, to 0.15mwhen the higher density data was used. Improvements in the uncertainty of the measurement of tree location, 0.80 m to 0.53 m, and crown width, 0.69 m to 0.61 m are also shown.

Development of a UAV-LiDAR System with Application to Forest Inventory

This paper provides an overview of the most significant existing methods for indoor positioning on a contemporary smartphone. The approaches include Wi-Fi and Bluetooth based positioning, magnetic field fingerprinting, map aided navigation using building floor plans, and aiding from self-contained sensors. Wi-Fi and Bluetooth based positioning methods considered in this survey are fingerprint approaches that determine a user's position using a database of radio signal strength measurements that were collected earlier at known locations. Magnetic field fingerprinting can be used in an information fusion algorithm to improve positioning. The map-matching algorithms include application of wall constraints, topological indoor maps, and building geometry for heading correction. Finally, methods for step counting, step length and direction estimation, orientation tracking, motion classification, transit mode detection, and floor change detection in multi-storey buildings are discussed.

A Survey of Selected Indoor Positioning Methods for Smartphones

The objective of this investigation was to develop and investigate methods for point cloud generation by image matching using aerial image data collected by quadrocopter type micro unmanned aerial vehicle (UAV) imaging systems. Automatic generation of high-quality, dense point clouds from digital images by image matching is a recent, cutting-edge step forward in digital photogrammetric technology. The major components of the system for point cloud generation are a UAV imaging system, an image data collection process using high image overlaps, and post-processing with image orientation and point cloud generation. Two post-processing approaches were developed: one of the methods is based on Bae Systems’ SOCET SET classical commercial photogrammetric software and another is built using Microsoft®’s Photosynth™ service available in the Internet. Empirical testing was carried out in two test areas. Photosynth processing showed that it is possible to orient the images and generate point clouds fully automatically without any a priori orientation information or interactive work. The photogrammetric processing line provided dense and accurate point clouds that followed the theoretical principles of photogrammetry, but also some artifacts were detected. The point clouds from the Photosynth processing were sparser and noisier, which is to a large extent due to the fact that the method is not optimized for dense point cloud generation. Careful photogrammetric processing with self-calibration is required to achieve the highest accuracy. Our results demonstrate the high performance potential of the approach and that with rigorous processing it is possible to reach results that are consistent with theory. We also point out several further research topics. Based on theoretical and empirical results, we give recommendations for properties of imaging sensor, data collection and processing of UAV image data to ensure accurate point cloud generation.

https://www.mdpi.com/1424-8220/12/1/453/pdf

Point Cloud Generation from Aerial Image Data Acquired by a Quadrocopter Type Micro Unmanned Aerial Vehicle and a Digital Still Camera

Current WiFi positioning systems (WPSs) require databases – such as locations of WiFi access points and propagation parameters, or a radio map – to assist with positioning. Typically, procedures for building such databases are time-consuming and labour-intensive. In this paper, two autonomous crowdsourcing systems are proposed to build the databases on handheld devices by using our designed algorithms and an inertial navigation solution from a Trusted Portable Navigator (T-PN). The proposed systems, running on smartphones, build and update the database autonomously and adaptively to account for the dynamic environment. To evaluate the performance of automatically generated databases, two improved WiFi positioning schemes (fingerprinting and trilateration) corresponding to these two database building systems, are also discussed. The main contribution of the paper is the proposal of two crowdsourcing-based WPSs that eliminate the various limitations of current crowdsourcing-based systems which (a) require a floor plan or GPS, (b) are suitable only for specific indoor environments, and (c) implement a simple MEMS-based sensors’ solution. In addition, these two WPSs are evaluated and compared through field tests. Results in different test scenarios show that average positioning errors of both proposed systems are all less than 5.75 m.

Evaluation of Two WiFi Positioning Systems Based on Autonomous Crowdsourcing of Handheld Devices for Indoor Navigation

This paper introduces a comparison of training algorithms of radial basis function (RBF) neural networks for classification purposes. RBF networks provide effective solutions in many science and engineering fields. They are especially popular in the pattern classification and signal processing areas. Several algorithms have been proposed for training RBF networks. The Artificial Bee Colony (ABC) algorithm is a new, very simple and robust population based optimization algorithm that is inspired by the intelligent behavior of honey bee swarms. The training performance of the ABC algorithm is compared with the Genetic algorithm, Kalman filtering algorithm and gradient descent algorithm. In the experiments, not only well known classification problems from the UCI repository such as the Iris, Wine and Glass datasets have been used, but also an experimental setup is designed and inertial sensor based terrain classification for autonomous ground vehicles was also achieved. Experimental results show that the use of the ABC algorithm results in better learning than those of others.

A Comparison of RBF Neural Network Training Algorithms for Inertial Sensor Based Terrain Classification

Seamless navigation and mapping using an INS/GNSS/grid-based SLAM semi-tightly coupled integration scheme

Digital mobile mapping, which integrates digital imaging with direct geo-referencing, has developed rapidly over the past fifteen years. Direct geo-referencing is the determination of the time-variable position and orientation parameters for a mobile digital imager. The most common technologies used for this purpose today are satellite positioning using Global Positioning System (GPS) and Inertial Navigation System (INS) using an Inertial Measurement Unit (IMU). They are usually integrated in such a way that the GPS receiver is the main position sensor, while the IMU is the main orientation sensor. The Kalman Filter (KF) is considered as the optimal estimation tool for real-time INS/GPS integrated kinematic position and orientation determination. An intelligent hybrid scheme consisting of an Artificial Neural Network (ANN) and KF has been proposed to overcome the limitations of KF and to improve the performance of the INS/GPS integrated system in previous studies. However, the accuracy requirements of general mobile mapping applications can’t be achieved easily, even by the use of the ANN-KF scheme. Therefore, this study proposes an intelligent position and orientation determination scheme that embeds ANN with conventional Rauch-Tung-Striebel (RTS) smoother to improve the overall accuracy of a MEMS INS/GPS integrated system in post-mission mode. By combining the Micro Electro Mechanical Systems (MEMS) INS/GPS integrated system and the intelligent ANN-RTS smoother scheme proposed in this study, a cheaper but still reasonably accurate position and orientation determination scheme can be anticipated.

/pdf/an-artificial-neural-network-embedded-position-and-4i5wsarliw.pdf

An Artificial Neural Network Embedded Position and Orientation Determination Algorithm for Low Cost MEMS INS/GPS Integrated Sensors

With the increasing demands for seamless landvehicle navigation, systems with robust performance are required in highly urbanized areas. The traditional INS/GNSS integration is widely applied to solve this issue. However, the system still suffers from bad GNSS signal reception and INS time accumulated errors that seamlessness and stability are difficult to maintain. In this study, the performance of the low cost INS/GNSS with aiding sensors, such as an odometer and barometer, was evaluated for both the loosely-coupled (LC) scheme and tightly-coupled (TC) scheme. Moreover, considering barometric error accumulation, a vehicle-behavior based drift control method has been proposed. An experiment was conducted under harsh GNSS-degraded scenarios to assess the characteristics and performance for different sensor combinations, using single constellation (GPS) with single-frequency (L1 band) measurement. Overall, the TC scheme without additional strategies in detecting abnormal measurement encounters more challenges to achieve stable performance. In an INS/GNSS/barometer system with the proposed drift control method, error accumulation under unpredictable environmental changes was successfully mitigated in both schemes. The proposed method can maintain a height accuracy of 2-meter level root mean square even after a long term operation. In an INS/GNSS/odometer combination, improvements were observed in the horizontal and vertical direction for both schemes. According to statistical analysis, an INS/GNSS/odometer/barometercombination shows 16.57% and 6.11% in the horizontal, and 30.71% and 71.28% in the vertical for the LC scheme and TC scheme, compared with an INS/GNSS combination.

Assessment for INS/GNSS/Odometer/Barometer Integration in Loosely-Coupled and Tightly-Coupled Scheme in a GNSS-Degraded Environment

A method and apparatus for providing an enhanced navigation solution for cycling applications is described herein. The navigation solution is about a device within a platform, which is a cycling platform such as for example a bicycle, a tricycle, or a unicycle amongst others. The device can be in any orientation with respect to the platform (such as for example in any location or orientation on the body of the cyclist). The device includes a sensor assembly. The sensors in the device may be for example, accelerometers, gyroscopes, magnetometers, barometer among others. The present method and apparatus can work whether in the presence or in the absence of navigational information updates (such as, for example, Global Navigation Satellite System (GNSS) or WiFi positioning).

Method and apparatus for improved navigation for cycling

Mobile mapping systems have been widely applied for acquiring spatial information in applications such as spatial information systems and 3D city models. Nowadays the most common technologies used for positioning and orientation of a mobile mapping system include a Global Positioning System (GPS) as the major positioning sensor and an Inertial Navigation System (INS) as the major orientation sensor. In the classical approach, the limitations of the Kalman Filter (KF) method and the overall price of multi-sensor systems have limited the popularization of most land-based mobile mapping applications. Although intelligent sensor positioning and orientation schemes consisting of Multi-layer Feed-forward Neural Networks (MFNNs), one of the most famous Artificial Neural Networks (ANNs), and KF/smoothers, have been proposed in order to enhance the performance of low cost Micro Electro Mechanical System (MEMS) INS/GPS integrated systems, the automation of the MFNN applied has not proven as easy as initially expected. Therefore, this study not only addresses the problems of insufficient automation in the conventional methodology that has been applied in MFNN-KF/smoother algorithms for INS/GPS integrated systems proposed in previous studies, but also exploits and analyzes the idea of developing alternative intelligent sensor positioning and orientation schemes that integrate various sensors in more automatic ways. The proposed schemes are implemented using one of the most famous constructive neural networks—the Cascade Correlation Neural Network (CCNNs)—to overcome the limitations of conventional techniques based on KF/smoother algorithms as well as previously developed MFNN-smoother schemes. The CCNNs applied also have the advantage of a more flexible topology compared to MFNNs. Based on the experimental data utilized the preliminary results presented in this article illustrate the effectiveness of the proposed schemes compared to smoother algorithms as well as the MFNN-smoother schemes.

Hsiu Wen Chang

Papers

Seamless navigation and mapping using an INS/GNSS/grid-based SLAM semi-tightly coupled integration scheme

An Artificial Neural Network Embedded Position and Orientation Determination Algorithm for Low Cost MEMS INS/GPS Integrated Sensors

Assessment for INS/GNSS/Odometer/Barometer Integration in Loosely-Coupled and Tightly-Coupled Scheme in a GNSS-Degraded Environment

Method and apparatus for improved navigation for cycling

Intelligent Sensor Positioning and Orientation Through Constructive Neural Network-Embedded INS/GPS Integration Algorithms