Abstract: In global navigation satellite system (GNSS) denied settings, such as indoor environments, autonomous mobile robots are often limited to dead-reckoning navigation techniques to determine their position, velocity, and attitude (PVA). Localization is typically accomplished by employing an inertial measurement unit (IMU), which, while precise in nature, accumulates errors rapidly and severely degrades the localization solution. Standard sensor fusion methods, such as Kalman filtering, aim to fuse precise IMU measurements with accurate aiding sensors to establish a precise and accurate solution. In indoor environments, where GNSS and no other a priori information is known about the environment, effective sensor fusion is difficult to achieve, as accurate aiding sensor choices are sparse. However, an opportunity arises by employing a depth camera in the indoor environment. A depth camera can capture point clouds of the surrounding floors and walls. Extracting attitude from these surfaces can serve as an accurate aiding source, which directly combats errors that arise due to gyroscope imperfections. This configuration for sensor fusion leads to a dramatic reduction of PVA error compared to traditional aiding sensor configurations. This paper provides the theoretical basis for the depth camera aiding sensor method, initial expectations of performance benefit via simulation, and hardware implementation thus verifying its veracity. Hardware implementation is performed on the Quanser Qbot 2™ mobile robot, with a Vector-Nav VN-200™ IMU and Kinect™ camera from Microsoft.
Abstract: Surf is an increasingly popular sport and its performance evaluation is often qualitative. This work aims at using a smartphone to collect and analyze the GPS and inertial sensors data in order to obtain quantitative metrics of the surfing performance. Two approaches are compared for detection of wave rides, computing the number of waves rode in a surfing session, the starting time of each wave and its duration. The first approach is based on computing the velocity from the Global Positioning System (GPS) signal and finding the velocity thresholds that allow identifying the start and end of each wave ride. The second approach adds information from the Inertial Measurement Unit (IMU) of the smartphone, to the velocity thresholds obtained from the GPS unit, to determine the start and end of each wave ride. The two methods were evaluated using GPS and IMU data from two surfing sessions and validated with similar metrics extracted from video data collected from the beach. The second method, combining GPS and IMU data, was found to be more accurate in determining the number of waves, start time and duration. This paper shows that it is feasible to use smartphones for quantification of performance metrics during surfing. In particular, detection of the waves rode and their duration can be accurately determined using the smartphone GPS and IMU.
Abstract: Two multisensor system architectures for navigation
and guidance of small Unmanned Aircraft (UA) are presented and
compared. The main objective of our research is to design a compact,
light and relatively inexpensive system capable of providing the
required navigation performance in all phases of flight of small UA,
with a special focus on precision approach and landing, where Vision
Based Navigation (VBN) techniques can be fully exploited in a
multisensor integrated architecture. Various existing techniques for
VBN are compared and the Appearance-Based Navigation (ABN)
approach is selected for implementation. Feature extraction and
optical flow techniques are employed to estimate flight parameters
such as roll angle, pitch angle, deviation from the runway centreline
and body rates. Additionally, we address the possible synergies of
VBN, Global Navigation Satellite System (GNSS) and MEMS-IMU
(Micro-Electromechanical System Inertial Measurement Unit)
sensors, and the use of Aircraft Dynamics Model (ADM) to provide
additional information suitable to compensate for the shortcomings of
VBN and MEMS-IMU sensors in high-dynamics attitude
determination tasks. An Extended Kalman Filter (EKF) is developed
to fuse the information provided by the different sensors and to
provide estimates of position, velocity and attitude of the UA
platform in real-time. The key mathematical models describing the
two architectures i.e., VBN-IMU-GNSS (VIG) system and VIGADM
(VIGA) system are introduced. The first architecture uses VBN
and GNSS to augment the MEMS-IMU. The second mode also
includes the ADM to provide augmentation of the attitude channel.
Simulation of these two modes is carried out and the performances of
the two schemes are compared in a small UA integration scheme (i.e.,
AEROSONDE UA platform) exploring a representative cross-section
of this UA operational flight envelope, including high dynamics
manoeuvres and CAT-I to CAT-III precision approach tasks.
Simulation of the first system architecture (i.e., VIG system) shows
that the integrated system can reach position, velocity and attitude
accuracies compatible with the Required Navigation Performance
(RNP) requirements. Simulation of the VIGA system also shows
promising results since the achieved attitude accuracy is higher using
the VBN-IMU-ADM than using VBN-IMU only. A comparison of
VIG and VIGA system is also performed and it shows that the
position and attitude accuracy of the proposed VIG and VIGA
systems are both compatible with the RNP specified in the various
UA flight phases, including precision approach down to CAT-II.
Abstract: Methods for measuring or estimating ground shape by a laser range finder and a vision sensor (Exteroceptive sensors) have critical weaknesses in terms that these methods need a prior database built to distinguish acquired data as unique surface conditions for driving. Also, ground information by Exteroceptive sensors does not reflect the deflection of ground surface caused by the movement of UGVs. Therefore, this paper proposes a method of recognizing exact and precise ground shape using an Inertial Measurement Unit (IMU) as a proprioceptive sensor. In this paper, firstly this method recognizes the attitude of a robot in real-time using IMU and compensates attitude data of a robot with angle errors through analysis of vehicle dynamics. This method is verified by outdoor driving experiments of a real mobile robot.
Abstract: Land vehicle navigation system technology is a subject of great interest today. Global Positioning System (GPS) is a common choice for positioning in such systems. However, GPS alone is incapable of providing continuous and reliable positioning, because of its inherent dependency on external electromagnetic signals. Inertial Navigation is the implementation of inertial sensors to determine the position and orientation of a vehicle. As such, inertial navigation has unbounded error growth since the error accumulates at each step. Thus in order to contain these errors some form of external aiding is required. The availability of low cost Micro-Electro-Mechanical-System (MEMS) inertial sensors is now making it feasible to develop Inertial Navigation System (INS) using an inertial measurement unit (IMU), in conjunction with GPS to fulfill the demands of such systems. Typically IMU’s are very expensive systems; however this INS will use “low cost” components. Unfortunately with low cost also comes low performance and is the main reason for the inclusion of GPS and Kalman filtering into the system. The aim of this paper is to develop a GPS/MEMS INS integrated system, which is able to provide a navigation solution with accuracy levels appropriate for land vehicle navigation. The primary piece of equipment used was a MEMS-based Crista IMU (from Cloud Cap Technology Inc.) and a Garmin GPS 18 PC (which is both a receiver and antenna). The integration of GPS with INS can be implemented using a Kalman filter in loosely coupled mode. In this integration mode the INS error states, together with any navigation state (position, velocity, and attitude) and other unknown parameters of interest, are estimated using GPS measurements. All important equations regarding navigation are presented along with discussion.
Abstract: In this paper, the detection of a fault in the Global Positioning System (GPS) measurement is addressed. The class of faults considered is a bias in the GPS pseudorange measurements. This bias is modeled as an unknown constant. The fault could be the result of a receiver fault or signal fault such as multipath error. A bias bank is constructed based on set of possible fault hypotheses. Initially, there is equal probability of occurrence for any of the biases in the bank. Subsequently, as the measurements are processed, the probability of occurrence for each of the biases is sequentially updated. The fault with a probability approaching unity will be declared as the current fault in the GPS measurement. The residual formed from the GPS and Inertial Measurement Unit (IMU) measurements is used to update the probability of each fault. Results will be presented to show the performance of the presented algorithm.