Multiple Integrated Navigation Sensors for Improving Occupancy Grid FastSLAM

An autonomous vehicle must accurately observe its location within the environment to interact with objects and accomplish its mission. When its environment is unknown, the vehicle must construct a map detailing its surroundings while using it to maintain an accurate location. Such a vehicle is faced with the circularly defined Simultaneous Localization and Mapping (SLAM) problem. However difficult, SLAM is a critical component of autonomous vehicle exploration with applications to search and rescue. To current knowledge, this research presents the first SLAM solution to integrate stereo cameras, inertial measurements, and vehicle odometry into a Multiple Integrated Navigation Sensor (MINS) path. The implementation combines the MINS path with LIDAR to observe and map the environment using the FastSLAM algorithm. In real-world tests, a mobile ground vehicle equipped with these sensors completed a 140 meter loop around indoor hallways. This SLAM solution produces a path that closes the loop and remains within 1 meter of truth, reducing the error 92% from an image-inertial navigation system and 79% from odometry FastSLAM

A Meta-Review of Indoor Positioning Systems

An accurate and reliable Indoor Positioning System (IPS) applicable to most indoor scenarios has been sought for many years. The number of technologies, techniques, and approaches in general used in IPS proposals is remarkable. Such diversity, coupled with the lack of strict and verifiable evaluations, leads to difficulties for appreciating the true value of most proposals. This paper provides a meta-review that performed a comprehensive compilation of 62 survey papers in the area of indoor positioning. The paper provides the reader with an introduction to IPS and the different technologies, techniques, and some methods commonly employed. The introduction is supported by consensus found in the selected surveys and referenced using them. Thus, the meta-review allows the reader to inspect the IPS current state at a glance and serve as a guide for the reader to easily find further details on each technology used in IPS. The analyses of the meta-review contributed with insights on the abundance and academic significance of published IPS proposals using the criterion of the number of citations. Moreover, 75 works are identified as relevant works in the research topic from a selection of about 4000 works cited in the analyzed surveys

Hardware design for in-mine positioning system

This thesis describes the hardware design of a positioning system which locates a vehicle relative to a digital map of an underground mine. The mines of interest are potash mines of Saskatchewan, and they are at a depth of approximately 1000 meters and they cover an area larger than 10 kilometers by 10 kilometers. An important application of an in-mine positioning system is tracking a ground penetrating radar system. Ground penetrating radar is used to determine the current condition of the mine ceiling and to evaluate its risk of delamination. A ground penetrating radar system is driven along a mine tunnel and measurements are logged. It is necessary to record position information along with the radar signal and this can be done with the aid of a positioning system. The design and evaluation of the hardware system that supports a positioning system, which can locate a vehicle inside a mine tunnel with reasonable accuracy and cost is described in this thesis. The hardware system includes a dead reckoning system (DRS), which is built using MEMS (Micro Electro Mechanical System) accelerometer and gyroscope sensors and ultrasonic distance sensors, along with a data acquisition system

Development of a Sensing System for Underground Optic Fiber Cable Conduit Mapping

The motivation of this research is to obtain an accurate three-dimensional (3D) layout of an underground conduit, which may be beneficial to optic fiber cable installers and engineers. A newly designed algorithm for 3D position tracking with the help of an inertial sensor and an encoder has been developed. Two types of representations (Euler angle and Quaternion) for orientation and rotation are also introduced, followed by several data pre-processing procedures. A sensing fusion method is utilized to overcome the accumulated errors introduced by the sensor drifting. Considering the application of 3D underground duct mapping in this research, a sensing system using the newly designed algorithm was designed and analyzed. Additional information, such as the orientation and position of the starting and ending points, are integrated into the algorithm to correct the sensing drifting and refine the position estimation. To verify and demonstrate the design of the algorithm and sensing system for 3D underground duct mapping, an experimental test-bed based on the sensing system design, which consists of an IMU, a duct rodder and a fiber blower, was developed. Experiments on three different layouts of the conduit were conducted and analyzed to demonstrate the feasibility and efficiency of the newly developed algorithm and the sensing system design

Multi-sensor fusion based on multiple classifier systems for human activity identification

Multimodal sensors in healthcare applications have been increasingly researched because it facilitates automatic and comprehensive monitoring of human behaviors, high-intensity sports management, energy expenditure estimation, and postural detection. Recent studies have shown the importance of multi-sensor fusion to achieve robustness, high-performance generalization, provide diversity and tackle challenging issue that maybe difficult with single sensor values. The aim of this study is to propose an innovative multi-sensor fusion framework to improve human activity detection performances and reduce misrecognition rate. The study proposes a multi-view ensemble algorithm to integrate predicted values of different motion sensors. To this end, computationally efficient classification algorithms such as decision tree, logistic regression and k-Nearest Neighbors were used to implement diverse, flexible and dynamic human activity detection systems. To provide compact feature vector representation, we studied hybrid bio-inspired evolutionary search algorithm and correlation-based feature selection method and evaluate their impact on extracted feature vectors from individual sensor modality. Furthermore, we utilized Synthetic Over-sampling minority Techniques (SMOTE) algorithm to reduce the impact of class imbalance and improve performance results. With the above methods, this paper provides unified framework to resolve major challenges in human activity identification. The performance results obtained using two publicly available datasets showed significant improvement over baseline methods in the detection of specific activity details and reduced error rate. The performance results of our evaluation showed 3% to 24% improvement in accuracy, recall, precision, F-measure and detection ability (AUC) compared to single sensors and feature-level fusion. The benefit of the proposed multi-sensor fusion is the ability to utilize distinct feature characteristics of individual sensor and multiple classifier systems to improve recognition accuracy. In addition, the study suggests a promising potential of hybrid feature selection approach, diversity-based multiple classifier systems to improve mobile and wearable sensor-based human activity detection and health monitoring system. - 2019, The Author(s).This research is supported by University of Malaya BKP Special Grant no vote BKS006-2018.Scopu

Training Gaussian Process Regression Models Using Optimized Trajectories

Quadrotor helicopters and robot manipulators are used widely for both research and industrial applications. Both quadrotors and manipulators are difficult to model. Quadrotors have complex dynamic models, especially at high speeds. Obtaining an accurate model of manipulator dynamics is often difficult, due to inaccurate values for link parameters and dynamics such as friction which are difficult to model accurately. Supervised learning methods such as Gaussian Process Regression (GPR) have been used to learn the inverse dynamics of a system. These methods can estimate a dynamic model from experimental data without requiring the structure of the model to be known, and can be used online to update the model if the system changes over time. This approach has been used to learn the inverse dynamics of a manipulator, but has not yet been applied to quadrotors. In addition, collecting training data for supervised learning can be difficult and time consuming, and poor or inadequate training data may result in an inaccurate model. Another problem frequently encountered when using GPR to learn the model of a system is the large computational cost of using GPR. A number of sparse approximations of GPR exist to deal with this issue, but it is not clear which sparse approximation results in the best performance, particularly when training data is being added incrementally. This thesis proposes a method for systematically collecting training data for a GPR model. The trajectory used to collect training data is parameterized, and the parameters are optimized to maximize the GPR variance over the trajectory. This approach is tested both in simulation and experimentally for a quadrotor, and in experiments on a 4-DOF manipulator. Optimizing the training trajectories is shown to reduce the amount of training data required to learn the model of a system. The thesis also compares three sparse approximations of GPR: the dictionary approach, Sparse Spectrum GPR (SSGP) and simple downsampling of the training data to reduce the size of the training data set. Using a dictionary is found to provide the best performance, even when the dictionary contains a very small subset of the available data. Finally, all GPR models have hyperparameters, which have a significant impact on the prediction made by the GP model. Training these hyperparameters is important for getting accurate predictions. This thesis evaluates different methods of hyperparameter training on a 4-DOF manipulator to determine the most effective method of training the hyperparameters. For SSGP, the best hyperparameter training strategy is to reinitialize and train the hyperparameters after each trajectory. SSGP is also observed to be highly sensitive to the number of iterations of gradient descent used in hyperparameter training; too many iterations of gradient descent leads to overfitting and poor predictions. When using a dictionary, the best hyperparameter training method is to retrain the hyperparameters after each trajectory, using the previous hyperparameters as the initial starting point