CES-515 Towards Localization and Mapping of Autonomous Underwater Vehicles: A Survey

Autonomous Underwater Vehicles (AUVs) have been used for a huge number of tasks ranging from commercial, military and research areas etc, while the fundamental function of a successful AUV is its localization and mapping ability. This report aims to review the relevant elements of localization and mapping for AUVs. First, a brief introduction of the concept and the historical development of AUVs is given; then a relatively detailed description of the sensor system used for AUV navigation is provided. As the main part of the report, a comprehensive investigation of the simultaneous localization and mapping (SLAM) for AUVs are conducted, including its application examples. Finally a brief conclusion is summarized

Low-cost RPAS navigation and guidance system using Square Root Unscented Kalman Filter

Multi-Sensor Data Fusion (MSDF) techniques involving satellite and inertial-based sensors are widely adopted to improve the navigation solution of a number of mission- and safety-critical tasks. Such integrated Navigation and Guidance Systems (NGS) currently do not meet the required level of performance in all flight phases of small Remotely Piloted Aircraft Systems (RPAS). In this paper an innovative Square Root-Unscented Kalman Filter (SR-UKF) based NGS is presented and compared with a conventional UKF governed design. The presented system architectures adopt state-of-the-art information fusion approach based on a number of low-cost sensors including; Global Navigation Satellite Systems (GNSS), Micro-Electro-Mechanical System (MEMS) based Inertial Measurement Unit (IMU) and Vision Based Navigation (VBN) sensors. Additionally, an Aircraft Dynamics Model (ADM), which is essentially a knowledge based module, is employed to compensate for the MEMS-IMU sensor shortcomings in high-dynamics attitude determination tasks. The ADM acts as a virtual sensor and its measurements are processed with non-linear estimation in order to increase the operational validity time. An improvement in the ADM navigation state vector (i.e., position, velocity and attitude) measurements is obtained, thanks to the accurate modeling of aircraft dynamics and advanced processing techniques. An innovative SR-UKF based VBN-IMU-GNSS-ADM (SR-U-VIGA) architecture design was implemented and compared with a typical UKF design (U-VIGA) in a small RPAS (AEROSONDE) integration arrangement exploring a representative cross-section of the operational flight envelope. The comparison of position and attitude data shows that the SR-U-VIGA and U-VIGA NGS fulfill the relevant RNP criteria, including precision approach tasks

The Estimation Methods for an Integrated INS/GPS UXO Geolocation System

This work was supported by a project funded by the US Army Corps of Engineers, Strategic Environment Research and Development Program, contract number W912HQ- 08-C-0044.This report was also submitted to the Graduate School of the Ohio State University in partial fulfillment of the PhD degree in Geodetic Science.Unexploded ordnance (UXO) is the explosive weapons such as mines, bombs, bullets, shells and grenades that failed to explode when they were employed. In North America, especially in the US, the UXO is the result of weapon system testing and troop training by the DOD. The traditional UXO detection method employs metal detectors which measure distorted signals of local magnetic fields. Based on detected magnetic signals, holes are dug to remove buried UXO. However, the detection and remediation of UXO contaminated sites using the traditional methods are extremely inefficient in that it is difficult to distinguish the buried UXO from the noise of geologic magnetic sources or anthropic clutter items. The reliable discrimination performance of UXO detection system depends on the employed sensor technology as well as on the data processing methods that invert the collected data to infer the UXO. The detection systems require very accurate positioning (or geolocation) of the detection units to detect and discriminate the candidate UXO from the non-hazardous clutter, greater position and orientation precision because the inversion of magnetic or EMI data relies on their precise relative locations, orientation, and depth. The requirements of position accuracy for MEC geolocation and characterization using typical state-of-the-art detection instrumentation are classified according to levels of accuracy outlined in: the screening level with position tolerance of 0.5 m (as standard deviation), area mapping (less than 0.05 m), and characterize and discriminate level of accuracy (less than 0.02m). The primary geolocation system is considered as a dual-frequency GPS integrated with a three dimensional inertial measurement unit (IMU); INS/GPS system. Selecting the appropriate estimation method has been the key problem to obtain highly precise geolocation of INS/GPS system for the UXO detection performance in dynamic environments. For this purpose, the Extended Kalman Filter (EKF) has been used as the conventional algorithm for the optimal integration of INS/GPS system. However, the newly introduced non-linear based filters can deal with the non-linear nature of the positioning dynamics as well as the non-Gaussian statistics for the instrument errors, and the non-linear based estimation methods (filtering/smoothing) have been developed and proposed. Therefore, this study focused on the optimal estimation methods for the highly precise geolocation of INS/GPS system using simulations and analyses of two Laboratory tests (cart-based and handheld geolocation system). First, the non-linear based filters (UKF and UKF) have been shown to yield superior performance than the EKF in various specific simulation tests which are designed similar to the UXO geolocation environment (highly dynamic and small area). The UKF yields 50% improvement in the position accuracy over the EKF particularly in the curved sections (medium-grade IMUs case). The UKF also performed significantly better than EKF and shows comparable improvement over the UKF when the IMU noise probability iii density function is symmetric and non-symmetric. Also, since the UXO detection survey does not require the real-time operations, each of the developed filters was modified to accommodate the standard Rauch-Tung-Striebel (RTS) smoothing algorithms. The smoothing methods are applied to the typical UXO detection trajectory; the position error was reduced significantly using a minimal number of control points. Finally, these simulation tests confirmed that tactical-grade IMUs (e.g. HG1700 or HG1900) are required to bridge gaps of high-accuracy ranging solution systems longer than 1 second. Second, these result of the simulation tests were validated from the laboratory tests using navigation-grade and medium-grade accuracy IMUs. To overcome inaccurate a priori knowledge of process noise of the system, the adaptive filtering methods have been applied to the EKF and UKF and they are called the AEKS and AUKS. The neural network aided adaptive nonlinear filtering/smoothing methods (NN-EKS and NN-UKS) which are augmented with RTS smoothing method were compared with the AEKS and AUKS. Each neural network-aided, adaptive filter/smoother improved the position accuracy in both straight and curved sections. The navigation grade IMU (H764G) can achieve the area mapping level of accuracy when the gap of control points is about 8 seconds. The medium grade IMUs (HG1700 and HG1900) with NN-AUKS can maintain less than 10cm under the same conditions as above. Also, the neural network aiding can decrease the difference of position error between the straight and the curved section. Third, in the previous simulation test, the UPF performed better than the other filters. However since the UPF needs a large number of samples to represent the a posteriori statistics in high-dimensional space, the RBPF can be used as an alternative to avoid the inefficiency of particle filter. The RBPF is tailored to precise geolocation for UXO detection using IMU/GPS system and yielded improved estimation results with a small number of samples. The handheld geolocation system using HG1900 with a nonlinear filter-based smoother can achieve the discrimination level of accuracy if the update rate of control points is less than 0.5Hz and 1Hz for the sweep and swing respectively. Also, the sweep operation is more preferred than the swing motion because the position accuracy of the sweep test was better than that of the swing test

Where Am I? SLAM for Mobile Machines on a Smart Working Site

The current optimization approaches of construction machinery are mainly based on internal sensors. However, the decision of a reasonable strategy is not only determined by its intrinsic signals, but also very strongly by environmental information, especially the terrain. Due to the dynamic changing of the construction site and the consequent absence of a high definition map, the Simultaneous Localization and Mapping (SLAM) offering the terrain information for construction machines is still challenging. Current SLAM technologies proposed for mobile machines are strongly dependent on costly or computationally expensive sensors, such as RTK GPS and cameras, so that commercial use is rare. In this study, we proposed an affordable SLAM method to create a multi-layer grid map for the construction site so that the machine can have the environmental information and be optimized accordingly. Concretely, after the machine passes by the grid, we can obtain the local information and record it. Combining with positioning technology, we then create a map of the interesting places of the construction site. As a result of our research gathered from Gazebo, we showed that a suitable layout is the combination of one IMU and two differential GPS antennas using the unscented Kalman filter, which keeps the average distance error lower than 2m and the mapping error lower than 1.3% in the harsh environment. As an outlook, our SLAM technology provides the cornerstone to activate many efficiency improvement approaches. View Full-Tex

Long-term experiments with an adaptive spherical view representation for navigation in changing environments

Real-world environments such as houses and offices change over time, meaning that a mobile robot’s map will become out of date. In this work, we introduce a method to update the reference views in a hybrid metric-topological map so that a mobile robot can continue to localize itself in a changing environment. The updating mechanism, based on the multi-store model of human memory, incorporates a spherical metric representation of the observed visual features for each node in the map, which enables the robot to estimate its heading and navigate using multi-view geometry, as well as representing the local 3D geometry of the environment. A series of experiments demonstrate the persistence performance of the proposed system in real changing environments, including analysis of the long-term stability

An Analytical Approach for Comparing Linearization Methods in EKF and UKF

The transformation of the mean and variance of a normally distributed random variable was considered through three different nonlinear functions: sin(x), cos(x), and xk, where k is a positive integer. The true mean and variance of the random variable after these transformations is theoretically derived within, and verified with respect to Monte Carlo experiments. These statistics are used as a reference in order to compare the accuracy of two different linearization techniques: analytical linearization used in the Extended Kalman Filter (EKF) and statistical linearization used in the Unscented Kalman Filter (UKF). This comparison demonstrated the advantage of using the unscented transformation in estimating the mean after transforming through each of the considered nonlinear functions. However, the variance estimation led to mixed results in terms of which linearization technique provided the best performance. As an additional analysis, the unscented transformation was evaluated with respect to its primary scaling parameter. A nonlinear filtering example is presented to demonstrate the usefulness of the theoretically derived results

All Source Sensor Integration Using an Extended Kalman Filter

The global positioning system (GPS) has become an ubiquitous source for navigation in the modern age, especially since the removal of selective availability at the beginning of this century. The utility of the GPS is unmatched, however GPS is not available in all environments. Heavy reliance on GPS for navigation makes the warfighter increasingly vulnerability as modern warfare continues to evolve. This research provides a method for incorporating measurements from a massive variety of sensors to mitigate GPS dependence. The result is the integration of sensor sets that encompass those examined in recent literature as well as some custom navigation devices. A full-state extended Kalman filter is developed and implemented, accommodating the requirements of the varied sensor sets and scenarios. Some 19 types of sensors are used in multiple quantities including inertial measurement units, single cameras and stereo pairs, 2D and 3D laser scanners, altimeters, 3-axis magnetometers, heading sensors, inclinometers, a stop sign sensor, an odometer, a step sensor, a ranging device, a signal of opportunity sensor, global navigation satellite system sensors, an air data computer, and radio frequency identification devices. Simulation data for all sensors was generated to test filter performance. Additionally, real data was collected and processed from an aircraft, ground vehicles, and a pedestrian. Measurement equations are developed to relate sensor measurements to the navigation states. Each sensor measurement is incorporated into the filter using the Kalman filter measurement update equations. Measurement types are segregated based on whether they observe instantaneous or accumulated state information. Accumulated state measurements are incorporated using delayed-state update equations. All other measurements are incorporated using the numerically robust UD update equations

An Analytical Approach for Comparing Linearization Methods in EKF and UKF

The transformation of the mean and variance of a normally distributed random variable was considered through three different nonlinear functions: sin(x), cos(x), and xk, where k is a positive integer. The true mean and variance of the random variable after these transformations is theoretically derived within, and verified with respect to Monte Carlo experiments. These statistics are used as a reference in order to compare the accuracy of two different linearization techniques: analytical linearization used in the Extended Kalman Filter (EKF) and statistical linearization used in the Unscented Kalman Filter (UKF). This comparison demonstrated the advantage of using the unscented transformation in estimating the mean after transforming through each of the considered nonlinear functions. However, the variance estimation led to mixed results in terms of which linearization technique provided the best performance. As an additional analysis, the unscented transformation was evaluated with respect to its primary scaling parameter. A nonlinear filtering example is presented to demonstrate the usefulness of the theoretically derived results