A Simple Observer for Gyro and Accelerometer Biases in Land Navigation Systems

Joint Charging and Routing Optimization for Electric Vehicle Navigation Systems

Localization of a vehicle is a key component for driving assistance or autonomous navigation. In this work, we propose a visual positioning system (VPS) for vehicle or mobile robot navigation. Different from general landmark-based or model-based approaches, which rely on some predefined known landmarks or a priori information about the environment, no assumptions on the prior knowledge of the scene are made. A stereo-based vision system is built for both extracting feature correspondences and recovering 3-D information of the scene from image sequences. Relative positions of the camera motion are then estimated by registering the 3-D feature points from two consecutive image frames. Localization of the vehicle is finally given by the reference to its initial position. I. INTRODUCTION Localization and tracking of a vehicle are major compo- nents for providing positions, directions and travel informa- tion to the driver. They also serve as key technologies for building autonomous navigation systems. Many approaches have been proposed for locating a mobile robot or a ve- hicle based on various techniques. The most commonly used methods include dead-reckoning techniques, navigation using active beacons, landmark-based navigation, map-based navigation, global positioning system (GPS), and vision- based positioning. Dead-reckoning is a procedure for determining the present location of a vehicle by advancing some previous position through known path and velocity information over a given period of time. Since an odometer and optical sensors for wheel direction detection are easily installed on a vehicle, dead-reckoning is usually a less expensive method for vehicle localization. However, the integration of incremental motion information over time will lead to accumulation of errors. In the case of long distance travel, accumulation of orientation error will make position errors diverge as the increase of driving time. Active beacons such as laser, sonar or radio can be used as media for vehicle or mobile robot navigation. These approaches use triangulation to measure the distance between a number of beacons and the mobile platform and then deter- mine the current location. The problems associated with this technique are the inaccuracy of the distance measurement caused by time-delay of the signals, and the installation and maintenance cost of a large number of beacons required for an area. Satellite based differential global positioning system (DGPS) is probably the most advanced and accurate method for identifying the position and orientation of an object. However, DGPS does not work well if the satellite signals are blocked. This situation commonly happens in the indoor environments, or urban areas with tall buildings, etc. In this work, we propose a visual positioning system (VPS) for vehicle or mobile robot navigation. Different from general landmark-based or model-based approaches which rely on some predefined known landmarks or ap rioriinfor- mation about the environment (e.g., 3-D models of buildings, objects, etc.), no assumptions on the prior knowledge of the scene are made. A stereo-based vision system is built for both extracting feature correspondences and recovering 3- D information of the scene from image sequences. A robust feature tracking method based on simultaneously considering the inter-frame images from the same camera and the stereo image pair from different cameras at a fixed time instant is developed. The relative position of the camera motion is then estimated by registering the 3-D feature points from two consecutive image frames. Our system includes the following basic modules:

Unmanned aerial vehicles (UAVs), also known as drones, are a cost-effective alternative to traditional surveying methods, and they can be used to collect geospatial data over inaccessible or hard-to-reach locations. UAV-integrated miniaturized remote sensing sensors such as hyperspectral and LiDAR sensors, which formerly operated on airborne and spaceborne platforms, have recently been developed. Their accuracies can still be guaranteed when incorporating pieces of equipment such as ground control points (GCPs) and field spectrometers. This study conducted three experiments for geometric and radiometric accuracy assessments of simultaneously acquired RGB, hyperspectral, and LiDAR data from a single mission. Our RGB and hyperspectral data generated orthorectified images based on direct georeferencing without any GCPs. Because of this, a base station is required for the post-processed Global Navigation Satellite System/Inertial Measurement Unit (GNSS/IMU) data. First, we compared the geometric accuracy of orthorectified RGB and hyperspectral images relative to the distance of the base station to determine which base station should be used. Second, point clouds could be generated from overlapped RGB images and a LiDAR sensor. We quantitatively and qualitatively compared RGB and LiDAR point clouds in this experiment. Lastly, we evaluated the radiometric quality of hyperspectral images, which is the most critical factor of the hyperspectral sensor, using reference spectra that was simultaneously measured by a field spectrometer. Consequently, the distance of the base station for post-processing the GNSS/IMU data was found to have no significant impact on the geometric accuracy, indicating that a dedicated base station is not always necessary. Our experimental results demonstrated geometric errors of less than two hyperspectral pixels without using GCPs, achieving a level of accuracy that is comparable to survey-level standards. Regarding the comparison of RGB- and LiDAR-based point clouds, RGB point clouds exhibited noise and lacked details; however, through the cleaning process, their vertical accuracy was found to be comparable with LiDAR’s accuracy. Although photogrammetry generated denser point clouds compared with LiDAR, the overall quality for extracting the elevation data greatly relies on factors such as the original image quality, including the image’s occlusions, shadows, and tie-points, for matching. Furthermore, the image spectra derived from hyperspectral data consistently demonstrated high radiometric quality without the need for in situ field spectrum information. This finding indicates that in situ field spectra are not always required to guarantee the radiometric quality of hyperspectral data, as long as well-calibrated targets are utilized.

This technical report consists of a collection of task analytic data to support heavy vehicle driver assessment and protocol development. Data were collected from professional drivers to provide insights into the following issues: the meaning of the term workload to heavy vehicle drivers (N = 41 drivers interviewed); the demand placed on drivers (N = 55) by various driving conditions using a psychological scaling approach; the safety criticality and difficulty of selected standard driving tasks (N = 30 drivers assessed); the perceptual, motor, and cognitive loads imposed by various tasks while on the road under various conditions (N = 9 drivers observed over-the-road during revenue runs); and a preliminary analysis of the key features of generic high-technology in-cab devices. When professional truck drivers speak of workload, they tend to talk in terms of time stress or stress caused by delays to their schedules. Various driving conditions were unidimensionally scaled in terms of order of impact on drivers, and those results are presented and discussed. Safety criticality and difficulty assessment results provided some validation for the scaling outcomes. Visual allocation measures in this study suggested that the use of common in-cab tasks would serve as baseline measures, based on the revenue runs. Finally, key features considered in evaluating voice communication systems, vehicle navigation and route guidance systems, single/integrated displays, and text communication devices were determined.

Aiming at solving the navigation and obstacle avoidance of the unmanned vehicle,the multi sensor data fusion technology and unmanned vehicle obstacle avoidance navigation algorithm were studied profoundly. According to the requirements of the application of unmanned vehicle navigation and obstacle avoidance system, multisensor data fusion technology was applied to unmanned vehicle navigation and obstacle avoidance control system. In addition, A*VFF navigation and obstacle avoidance algorithm based on fuzzy neural network was improved. Finally, through the construction of the simulation platform, simulation experiment of the unmanned vehicle obstacle avoidance navigation was completed, and a better route was planned for unmanned vehicle in a more complex environment. The results showed that it realized the autonomous navigation of unmanned vehicle and obstacle avoidance function. Based on the above findings, it is concluded that the application of artificial intelligence detection system has good performance.

To compare patients' radiation exposure, technical feasibility, imaging quality and complication rate of percutaneous lung biopsies (PLBs) performed with a low-dose (LD) CT protocol under guidance of an optical navigation system. Fifty-two consecutive patients with suspected malignant lung lesions were enrolled and randomised into group 1 (PLBs under the guidance of the navigation system) and group 2 (PLBs under the guidance navigation system with an LD protocol). Patients' demographics, lesion features, procedure-related variables and CT image quality for group 2 were recorded and compared. Technical success was 100 % in both groups. The radiation dose to patients' chest was significantly lower in group 2 than in group 1 (group 1: mean TDLP 206 ± 59 mGy·cm, ~ 3.5 ± 1.0 mSv; group 2: 54.2 ± 46.2 mGy·cm, ~ 0.92 ± 0.78 mSv; P < 0.0001). The PNX rate was 12 % in group 1 and 11.1 % in group 2. The haemoptysis rate was 8.0 % in group 1 and 3.7 % in group 2. CT image quality obtained in group 2 was always rated as adequate and as excellent in 15 cases (56.0 %). An optical navigation system with LD CT protocol is useful for performing lung biopsies with decreased patient radiation exposure. • Navigation systems are useful tools in percutaneous imaging-guided procedures. • For lung biopsies, low-dose (LD) CT protocols may be used. • Combining LD protocols with optical CT navigation results in significantly reduced radiation exposure.

In recent years, navigation systems have been widely used in various amusement parks and sightseeing spots. Navigation systems, such as car navigation systems and navigation applications in smartphones, are also used in our daily life. By using a navigation system, users can not only find their location but also browse information on nearby attractions such as restaurants or department stores; furthermore, it can tell users the route to lead them to their destination. In these situations users will set their destinations by using the navigation system, but in tourism or for events, users have no idea about the current conditions. On the other hand, organizers will have a hard time trying to control the crowd flow as more and more tourists use their own mobile devices to search for the information they want. In this research, we propose a navigation system to control the crowd flow and lead users to specific destinations or areas naturally by presenting suitable information to them. In this paper, we will explain the experiments performed using the proposed system during an event held in Osaka called "Osaka Mizube Bar". The results of the experiment confirmed that the behavior of users can be affected by controlling the rank list that they check on their devices; furthermore, users can be led to a specific destination or area by taking advantage of this function.

The most driving purpose is to traverse to the destination safely, efficiently, and comfortably. Two types of approaches could achieve the goals, including the static and dynamic approaches. In the static aspect, vehicles use the static road and traffic information to navigate. Conversely, in the dynamic aspect, vehicles adopt the dynamic information instead. However, both of the two approaches first require getting the vehicle’s location and then map the position on an e-map. Thus, this chapter first introduces some important vehicle location determination algorithms: the dead reckoning and global position system algorithms, in which the precision of location technologies are compared. Then, the map-matching algorithm is described in detail. Finally, various vehicle navigation approaches are detailed, in which the important topics include: the navigation architecture, the navigation routing algorithm, and navigation applications.

In our current research, we are developing a practical mobile vehicle navigation system which is capable of controlling multiple vehicles on the general environment. In order to realize the practical use, navigation system should have high accuracy and be low cost. Therefore, in this paper, we propose the vehicle navigation system which realizes vehicles navigation using high accuracy localization scheme by ceiling cameras with infrared filters and LED markers. Our key ideas are two topics. One topic is system integration which consists of high accuracy recognition system for LED marker and AGV controller with low cost. The other topic is novel AGV navigation scheme with high accuracy under occlusion condition. For practical use, mobile vehicles are required to continue its tasks with safety and high accuracy on temporary occlusion condition. Our developed system can navigate target vehicle based on estimation odometry error with simple algorithm. The performance of our proposed control system is experimented with our prototype.

Multi-source fusion constitutes a research focus in the navigation domain. This article focuses on the online estimation of the mounting angles between the body frame and vehicle frame within low-cost embedded vehicle navigation modules and the lever arm between the global satellite navigation system (GNSS) antenna/odometer and the inertial measurement unit (IMU). An online mounting angle error estimation algorithm, using odometers and IMU speeds, has been developed to estimate the angle errors while vehicles are in motion. At the same time, an online estimation algorithm model for the GNSS antenna lever arm and odometer lever arm was constructed. These two types of lever arms are used as the estimated states, and then Kalman filters are used to estimate them. The algorithm can simultaneously estimate the IMU mounting angle error, GNSS antenna arm, and odometer arm online. The experimental outcomes demonstrate that the lever arm estimation algorithm presented herein is effective for tactical and MEMS-level inertial navigation, with an estimation error of less than 2 cm. Meanwhile, the proposed online estimation of the mounting angle algorithm has an accuracy comparable to that of the post-processing algorithm. After making up the mounting angle and lever arm, we found that the position and speed precision of the multi-source fusion navigation systems were significantly improved. The results indicate that the proposed online estimation of mounting angle error and lever arm algorithm are effective and may promote the practical and widespread application of integrated navigation systems in vehicles. It solves the shortcomings of traditional methods, including the cumbersome and inaccurate manual measurement of the lever arm. It provides a technical solution for developing a more accurate and convenient low-cost vehicle navigation module.

Combining a global navigation satellite system (GNSS) and microelectromechanical technology-based inertial system (MINS) has become essential in land vehicle navigation systems. In an urban environment, the error of position output by GNSS receiver increases because of the blocking and reflection of the signal by buildings, which affects the positioning accuracy of the integrated system. In order to improve the positioning accuracy under these scenarios, this paper proposed a new robust method based on the estimation of the standard deviation of GNSS positioning error. In our study, a method for estimating the standard deviation <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\boldsymbol{\sigma}}^p$</tex-math></inline-formula> (or variance) of the position error of GNSS receiver is firstly proposed. Then a robust filtering method combined multi-factor scaling and bias estimation is proposed based on the estimation of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\boldsymbol{\sigma}}^p$</tex-math></inline-formula> . The simulations and vehicle navigation test show that the proposed method has a robust positioning effect. When <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\boldsymbol{\sigma}}^p$</tex-math></inline-formula> increases, the position accuracy is closed to the Sage-Husa filtering method with boundary constraint (SH-KF). Simulations also indicate that the proposed method has better robust effect than the traditional Kalman filter and SH-KF when a large bias error of position exists.

To solve the localization failure problem of terrain-aided navigation (TAN) system of the autonomous underwater vehicle (AUV) caused by large area of underwater flat terrain in the Arctic, a navigation system with relocation part is constructed to enhance the robustness of localization. The system uses particle filter to estimate the AUV’s position and reduce the nonlinear noise disturbance, and the prior motion information is added to avoid the mismatching caused by the similar altitude of low-resolution map. Based on the estimate data and the measured altitude data, the normalized innovation square (NIS) is used to evaluate the differentiation of terrain sequence, and the differentiation is used as a judgment of whether the AUV is in the switch location. A simulation experiment is carried out on the 500 m resolution underwater map of the Arctic. The results show that adding the prior motion information can restrain the divergence of the estimator; NIS can accurately reflect the sharp change of terrain sequence. After the relocation process, the AUV can still maintain the positioning accuracy within 2 km after running 50 km in the area including flat and rough terrain. This research solves the problem of localization errors in the Arctic flat terrain in the system level and provides a solution for the application of underwater navigation in the Arctic.

This work presents an improvement of an inertial-Doppler underwater navigation system for unmanned underwater vehicles (UUV) using a complementary range sonar. The inertial-Doppler navigation system is generally based on an inertial measurement unit (IMU) and a Doppler velocity log (DVL) accompanying a magnetic compass and a depth sensor. Although conventional navigation systems update bias errors of the inertial sensors, scale effects of the DVL, the estimated position slowly drifts as time passes. This paper proposes a measurement model with additional range sonar to improve the performance of the IMU-DVL navigation system for underwater vehicles. The proposed navigation model based on inertial navigation systems includes the scale effects, the bias errors of the DVL, and the error model of the range sonar, where the order of the system states is 21. An extended Kalman filter was adopted to propagate the error covariance, updated the measurement errors and correct the state equation when the external measurements are available. Simulation conducted with the 6-d.o.f. equations of motion of an AUV in lawn-mowing survey mode illustrates the effectiveness of the IMU-DVL navigation system assisted by the additional range measurement.

The maxillofacial area is prone to the lodging of foreign objects in tissue, which are often difficult to remove. We describe a patient in whom foreign objects that were lodged deep in facial tissue were removed by using an optical navigational guidance system. An 84-year-old man presented with wounds on his face after falling through a glass door. Computed tomography images showed multiple radiopaque markers in the pterygopalatine fossa and temporal region. An optical navigation system was used to locate the foreign objects, and the glass pieces that were embedded in the skin and deeper tissues were extracted by using a guided navigation system with the patient under general anesthesia. Thus, foreign objects lodged in the maxillofacial area were successfully removed by using a guided navigation system in a minimally invasive manner.

Emerging automotive engineering solutions extensively rely on navigation tools. Shortest path evaluation is core competency of optimal path planning, one of the most critical component of any navigation application. Fibonacci heap variant of state-of-the-art Dijkstra’s algorithm solve single source shortest path problems in asymptotic O(E + V log V) worst-case time. In spite Fibonacci heap is theoretically dominant in time efficiency, it might not work much better empirically with real-world data, because although large and intricate, the real-world road network graphs are sparse and the graph topology greatly affects the performance of the algorithms. SGO algorithm, a more efficient approch to address single source shortest path problem, is applied to solve optimal path planning problem in automotive navigation applications. Each road segment of the road graph leads to a particular location thus will hold unique distance values all the time. The SGO calculates the total distance from start location to the end of the each road segment and evaluates shortest path based on these calculations. The SGO algorithm and the Fibonacci and Binary heap variants of Dijkstra’s algorithm were tested on real-world intercity and intra-city road network graphs to evaluate running time efficiency of the algorithms in automotive navigation systems. The experimental results show that the SGO algorithm outperformed both the variants. The empirical superiority of the SGO algorithm suggests its usefulness in automotive navigation systems.