Car Navigation Research Articles

Indoor Unmanned Aerial Vehicle Navigation System Using LED Panels and QR Codes

In this study, we propose an unmanned aerial vehicle (UAV) navigation system using LED panels and QR codes as markers in an indoor environment. An LED panel can display various patterns; hence, we use it as a command presentation device for UAVs, and a QR code can embed various pieces of information, which is used as a sign to estimate the location of the UAV on the way of the flight path. In this paper, we present a navigation method from departure to destination positions in which an obstacle lies between them. In addition, we investigate the effectiveness of our proposed method using an actual UAV.

New Aspects of Progress in the Modernization of the Maritime Radio Direction Finders (RDF)

This paper as an author contribution introduces the implementation of the new aspects in the modernization of the ships Radio Direction Finders (RDF) and their modern principles and applications for shipborne and coastal navigation surveillance systems. The origin RDF receivers with the antenna installed onboard ships or aircraft were designed to identify radio sources that provide bearing the Direction Finding (DF) signals. The radio DF system or sometimes simply known as the DF technique is de facto a basic principle of measuring the direction of signals for determination of the ship's position. The position of a particular ship in coastal navigation can be obtained by two or more measurements of certain radio sources received from different unspecified locations of transmitters on the coast. In the past, the RDF devices were widely used as a radio navigation system for aircraft, vehicles, and ships in particular. However, the newly developed RDF devices can be used today as an alternative to the Radio – Automatic Identification System (R-AIS), Satellite – Automatic Identification System (S-AIS), Long Range Identification and Tracking (LRIT), radars, GNSS receivers, and another current tracking and positioning systems of ships. The development of a modern shipborne RDF for new positioning and surveillance applications, such as Search and Rescue (SAR), Man over board (MOB), ships navigation and collision avoidance, offshore applications, detection of research buoys and for costal vessels traffic control and management is described in this paper.

New Aspects of Progress in the Modernization of the Maritime Radio Direction Finders (RDF)

This paper as an author contribution introduces the implementation of the new aspects in the modernization of the ships Radio Direction Finders (RDF) and their modern principles and applications for shipborne and coastal navigation surveillance systems. The origin RDF receivers with the antenna installed onboard ships or aircraft were designed to identify radio sources that provide bearing the Direction Finding (DF) signals. The radio DF system or sometimes simply known as the DF technique is de facto a basic principle of measuring the direction of signals for determination of the ship's position. The position of a particular ship in coastal navigation can be obtained by two or more measurements of certain radio sources received from different unspecified locations of transmitters on the coast. In the past, the RDF devices were widely used as a radio navigation system for aircraft, vehicles, and ships in particular. However, the newly developed RDF devices can be used today as an alternative to the Radio – Automatic Identification System (R-AIS), Satellite – Automatic Identification System (S-AIS), Long Range Identification and Tracking (LRIT), radars, GNSS receivers, and another current tracking and positioning systems of ships. The development of a modern shipborne RDF for new positioning and surveillance applications, such as Search and Rescue (SAR), Man over board (MOB), ships navigation and collision avoidance, offshore applications, detection of research buoys and for costal vessels traffic control and management is described in this paper.

An Efficient Monte Carlo-Based Probabilistic Time-Dependent Routing Calculation Targeting a Server-Side Car Navigation System

Incorporating speed probability distribution to the computation of the route planning in car navigation systems guarantees more accurate and precise responses. In this paper, we propose a novel approach for selecting dynamically the number of samples used for the Monte Carlo simulation to solve the Probabilistic Time-Dependent Routing (PTDR) problem, thus improving the computation efficiency. The proposed method is used to determine in a proactive manner the number of simulations to be done to extract the travel-time estimation for each specific request, while respecting an error threshold as output quality level. The methodology requires a reduced effort on the application development side. We adopted an aspect-oriented programming language (LARA) together with a flexible dynamic autotuning library (mARGOt) respectively to instrument the code and to make decisions on tuning the number of samples to improve the execution efficiency. Experimental results demonstrate that the proposed adaptive approach saves a large fraction of simulations (between 36 and 81 percent) with respect to a static approach, while considering different traffic situations, paths and error requirements. Given the negligible runtime overhead of the proposed approach, the execution-time speedup is between 1.5x and 5.1x. This speedup is reflected at the infrastructure-level in terms of a reduction of 36 percent of the computing resources needed to support the whole navigation pipeline.

Navigable Space Construction from Sparse Noisy Point Clouds

Robot navigation with noisy perception is a fundamental but challenging task. We present a framework for creating navigable space from cluttered point clouds generated by low-end sensors with high sparsity and noise. Our method incrementally seeds and creates local convex regions free of obstacle points along robot's trajectory. Then a dense version of the point cloud is reconstructed through a map point regulation process where the original noisy map points are first projected onto a series of local convex hull surfaces, after which those points falling inside the convex hulls are culled. We have tested our proposed framework using a public autonomous driving dataset and a manually built structured environment for various performance evaluation, as well as inside our research building for ground robot navigation tasks. Our results reveal that the reconstructed navigable space has small volume loss (error) comparing with the ground truth, and the method is highly efficient, allowing real-time navigation computation.

Time- and Resource-Efficient Time-to-Collision Forecasting for Indoor Pedestrian Obstacles Avoidance.

As difficult vision-based tasks like object detection and monocular depth estimation are making their way in real-time applications and as more light weighted solutions for autonomous vehicles navigation systems are emerging, obstacle detection and collision prediction are two very challenging tasks for small embedded devices like drones. We propose a novel light weighted and time-efficient vision-based solution to predict Time-to-Collision from a monocular video camera embedded in a smartglasses device as a module of a navigation system for visually impaired pedestrians. It consists of two modules: a static data extractor made of a convolutional neural network to predict the obstacle position and distance and a dynamic data extractor that stacks the obstacle data from multiple frames and predicts the Time-to-Collision with a simple fully connected neural network. This paper focuses on the Time-to-Collision network’s ability to adapt to new sceneries with different types of obstacles with supervised learning.

An improved TDCP-GNSS/INS integration scheme considering small cycle slip for low-cost land vehicular applications

An improved global navigation satellite system (GNSS)/inertial navigation system (INS) tightly coupled scheme based on a time-differenced carrier phase (TDCP) was proposed for a low-cost vehicle navigation system. The proposed scheme aims to improve the navigation performance and computational efficiency of the system for low-cost GNSS receivers and low-accuracy INS. The traditional measurement model based on the TDCP has either high accuracy with a large computational load or high efficiency with low accuracy. Considering both the positioning accuracy and computational burden, an accurate and efficient measurement model was derived in this paper. In positioning based on the carrier phase, it is an indispensable task to handle the cycle slips, especially small ones. However, the cycle slip of a certain moment only contaminates the current TDCP measurement, but not the subsequent TDCP measurements. Based on this characteristic of the TDCP measurement, a novel cycle slip processing strategy was proposed for the TDCP-GNSS/INS integration scheme, in which cycle slip is directly handled as a gross error with a robust extended Kalman filter (EKF). The vehicular test has been conducted to evaluate the performance of the improved TDCP-GNSS/INS integration scheme. Experiment results show: (a) compared with the traditional position error parameter-based measurement model, the proposed measurement model can improve the accuracy of the horizontal and vertical position by 58.4% and 12.5%, respectively. Compared with the traditional velocity error parameter-based measurement model, the proposed measurement model does not improve the positioning accuracy, but improves the computational efficiency by 25.2%. (b) The strategy of treating cycle slip as a gross error is feasible in the TDCP-GNSS/INS tightly coupled navigation system. Although a small cycle slip can seriously affect the positioning performance of the system, the robust EKF can effectively avoid the long-term harm caused by small cycle slips.

Retracted: An unmanned aerial vehicles navigation system on the basis of pattern recognition applications—Review of implementation options and prospects for development

AbstractThe popularity of unmanned aerial vehicles (UAVs) kept growing over the past few decades. UAVs are widely used in various fields for monitoring, mapping, aerial photography, rescue operations, and so forth. However, the UAV navigation may be difficult in areas where global positioning systems is not available. This article presents an analytical review of the promising methods of creating a navigation system based on a pattern recognition algorithm. Linear algebra and mathematical morphology to describe the operation of pattern recognition applications in UAV's navigation systems, classification of images through profound research of intelligent navigation systems, analysis and digital image processing to identify options for the implementation of artificial intelligent systems of visual navigation were used to identify existing pattern recognition applications in the process of constructive systematization. It was shown that the UAV navigation requires objects in the image to be recognized and the distance to a possible obstacle to be estimated using deep learning method for image segmentation and depth map estimation. An obligatory initial point in the image recognition algorithm is the image preprocessing procedures based on machine vision techniques. The results of this study may be useful in developing efficient UAV applications for military and civil purposes.

Intelligent Vehicle Navigation System Based on Visual Detection and Positioning

This design is a set of intelligent vehicle navigation system which relies on visual detection and positioning. The image is processed and the coordinates of beacon and car body are extracted by the camera hanging above. The coordinate information is transmitted to the intelligent vehicle and fused with the intelligent vehicle data to determine its own environment and running state. The system integrates image processing, wireless communication and intelligent path planning to realize the task of accurate recognition and automatic tracing.

Double Deep Q-Learning and Faster R-CNN-Based Autonomous Vehicle Navigation and Obstacle Avoidance in Dynamic Environment.

Autonomous vehicle navigation in an unknown dynamic environment is crucial for both supervised- and Reinforcement Learning-based autonomous maneuvering. The cooperative fusion of these two learning approaches has the potential to be an effective mechanism to tackle indefinite environmental dynamics. Most of the state-of-the-art autonomous vehicle navigation systems are trained on a specific mapped model with familiar environmental dynamics. However, this research focuses on the cooperative fusion of supervised and Reinforcement Learning technologies for autonomous navigation of land vehicles in a dynamic and unknown environment. The Faster R-CNN, a supervised learning approach, identifies the ambient environmental obstacles for untroubled maneuver of the autonomous vehicle. Whereas, the training policies of Double Deep Q-Learning, a Reinforcement Learning approach, enable the autonomous agent to learn effective navigation decisions form the dynamic environment. The proposed model is primarily tested in a gaming environment similar to the real-world. It exhibits the overall efficiency and effectiveness in the maneuver of autonomous land vehicles.

Feasibility of Mobile Laser Scanning towards Operational Accurate Road Rut Depth Measurements.

This paper studied the applicability of the Roamer-R4DW mobile laser scanning (MLS) system for road rut depth measurement. The MLS system was developed by the Finnish Geospatial Research Institute (FGI), and consists of two mobile laser scanners and a Global Navigation Satellite System (GNSS)-inertial measurement unit (IMU) positioning system. In the study, a fully automatic algorithm was developed to calculate and analyze the rut depths, and verified in 64 reference pavement plots (1.0 m × 3.5 m). We showed that terrestrial laser scanning (TLS) data is an adequate reference for MLS-based rutting studies. The MLS-derived rut depths based on 64 plots resulted in 1.4 mm random error, which can be considered adequate precision for operational rutting depth measurements. Such data, also covering the area outside the pavement, would be ideal for multiple road environment applications since the same data can also be used in applications, from high-definition maps to autonomous car navigation and digitalization of street environments over time and in space.

UAV attitude calculation algorithm based on acceleration correction model

In order to improve the accuracy of attitude of unmanned aerial vehicle (UAV) navigation system in dynamic environment, an attitude calculation algorithm based on acceleration correction model is proposed. First, the acceleration correction model is established to calculate the estimated non-gravitational acceleration and external non-gravitational acceleration to modify the output value of the accelerometer, which reduces the influence of non-gravitational acceleration on the attitude calculation in dynamic environment. Then, the attitude calculation model based on Kalman filter is built, attitude angle calculated by corrected acceleration and magnetometer as measurement of filtering model, and the attitude calculation algorithm based on the acceleration correction model is designed. The experimental results show that the algorithm can reduce the interference of non-gravitational acceleration to attitude calculation, which avoids attitude angle divergence of UAV navigation system in dynamic environment, and improves the accuracy and anti-interference ability of UAV navigation system in dynamic environment.

Altitude on Cartographic Materials and Its Correction According to New Measurement Techniques

Determining the correct height of mountain peaks is vital for tourism, but it is also important as a reference point for devices equipped with GPS (Global Positioning System), e.g., watches or car navigation systems. The peak altitude data are part of geographic and geodetic information. As more modern technologies and equipment become available, their precisions should increase. However, verification of peak heights is usually only conducted for the highest, well-known mountains—lower peaks or mountain passes are rarely verified. Therefore, this study focuses on an investigation of 12 altitude points on a section of the longest and most famous touristic trail in Poland (the Main Beskid Trail), located in the Orava–Żywiec Beskids Mts (Mountains). The aim of this research is to measure and verify the heights of the 12 selected mountain peaks, in addition to evaluating the chosen methods based on the quality of the obtained data and determining their suitability and opportunities for use in further research. Measurements were obtained at the most specific height points—on the 12 highest points of the summits. This study compares two modern measurement techniques: the global navigation satellite system (GNSS) and light detection and ranging (LiDAR). The obtained results were later compared with those widely used on the internet and in printed materials (period covered: 1884–2015). This analysis demonstrates that lesser-known objects are rarely the subject of remeasurement and significant altitude errors may occur, primarily because the heights originated from a source in the past when modern methods were not available. Our findings indicate that the heights of the peaks presented in cartographic materials are inaccurate. The assumed heights should be corrected by direct measurements using modern techniques.

Homing Pigeon-Inspired Autonomous Navigation System for Unmanned Aerial Vehicles

This article proposes a novel homing behavior model of pigeons and the simulation results are presented. A homing pigeon-inspired autonomous navigation system for an unmanned aerial vehicle (UAV) is further achieved. Experimental results on the designed simulation platform show that UAV can finish an autonomous return voyage similar to the homing pigeon.

SCTM: a self-controlled touring and movement for industrial autonomous vehicle navigation

In recent days, the smart industry concept is becoming more prominent in industrial autonomous vehicle navigation systems. The need for service reliability and industrial management has led to various developing ideas in the industrial environment. Autonomous vehicles are commonly used for logistics and demand-aware migration of goods within the industry. In this article, the self-controlled touring and movement (SCTM) method is proposed to design, plan, and execute autonomous vehicle movements. The goal of this method is to improve the output of vehicle movements under controlled operating errors. For this purpose, the user requirement and outputs are identified and estimated beforehand for providing effective vehicle navigation plans. This error, balancing with the demands and outputs, is performed using classification learning wherein the changes of the vehicle’s movements are frequently updated. Further changes are performed within the completion of the tour plan for retaining operational efficiency of the autonomous vehicle. This method is verified using the following metrics: percent of changes, operating error, delay, balancing factor, and response. The proposed SCTM reduces error by 9.73% and delay by 17.1%, whereas it increases the balancing factor by 8.3% for different tour instances.

Target Tracking Using Reinforcement Learning and Neural Networks

Target tracking is a process that may find applications in different domains such as video surveillance, robot navigation and human computer interaction. In this work we have considered the problem of tracking a moving object in a multi agent environment. The environment is a rectangular space bounded by walls. The first agent is the target and it moves randomly in the space. The second agent should follow the target, keeping as close as possible without crashing with it. It uses sensors to detect the position of the target. The sensor readings give the distance and the angle from the target. We use reinforcement learning to train the tracker to detect any change in the movement of the target and stay within a certain range from it. Reinforcement learning is a form of machine learning in which the agent learns by interacting with the environment. By doing so, for each action taken, the agent receives a reward from the environment, which is used to determine positive or negative behaviour. The goal of the agent is to maximise the total reward received during the interaction. This form of machine learning has applications in different areas, such as: game solving with the most known game being AlphaGO; robotics, for design of hard-to engineer behaviours; traffic light control, personalized recommendations, etc. The sensor readings may have continuous values, making a very large state space. We approximate the value function using neural networks and use different reward functions for learning the best policy.

Target Tracking Using Reinforcement Learning and Neural Networks

Target tracking is a process that may find applications in different domains such as video surveillance, robot navigation and human computer interaction. In this work we have considered the problem of tracking a moving object in a multi agent environment. The environment is a rectangular space bounded by walls. The first agent is the target and it moves randomly in the space. The second agent should follow the target, keeping as close as possible without crashing with it. It uses sensors to detect the position of the target. The sensor readings give the distance and the angle from the target. We use reinforcement learning to train the tracker to detect any change in the movement of the target and stay within a certain range from it. Reinforcement learning is a form of machine learning in which the agent learns by interacting with the environment. By doing so, for each action taken, the agent receives a reward from the environment, which is used to determine positive or negative behaviour. The goal of the agent is to maximise the total reward received during the interaction. This form of machine learning has applications in different areas, such as: game solving with the most known game being AlphaGO; robotics, for design of hard-to engineer behaviours; traffic light control, personalized recommendations, etc. The sensor readings may have continuous values, making a very large state space. We approximate the value function using neural networks and use different reward functions for learning the best policy.

The Path Optimization Algorithm of Car Navigation System considering Node Attributes under Time-Invariant Network

Vehicle path planning plays a key role in the car navigation system. In actual urban traffic, the time spent at intersections accounts for a large proportion of the total time and cannot be ignored. Therefore, studying the shortest path planning problem considering node attributes has important practical significance. In this article, we study the vehicle path planning problem in time-invariant networks, with the minimum travel time from the starting node to the destination node as the optimization goal (including node time cost). Based on the characteristics of the problem, we construct the mathematical model. We propose a Reverse Order Labeling Algorithm (ROLA) based on the traditional Dijkstra algorithm to solve the problem; the correctness of the proposed algorithm is proved theoretically, and we analyse and give the time complexity of the ROLA and design a calculation example to verify the effectiveness of the algorithm. Finally, through extensive simulation experiments, we compare the performance of the proposed ROLA with several other existing algorithms. The experimental results show that the proposed algorithm has good stability and high efficiency.

Development and Improvement of Real-Time Navigation System for Laparoscopic Hepatectomy

Introduction: We are going to try development of real-time navigation system for laparoscopic hepatectomy, which resembles a car navigation system. We report our real-time navigation system and surgical procedure. Methods: Virtual images are reconstructed using “New-VES” system developed by Nagoya University Graduate School of Information Science. These images correspond to maps of car navigation system. Some of patient's body parts are registered in virtual images using a magnetic position sensor. Patient's body after registration corresponds to The Earth. A transmitter for magnetic position sensor, which corresponds to an artificial satellite, is placed about 30cm above patient's body. A micro magnetic sensor, which corresponds to GPS antenna, fixes on the handling part of laparoscope.Fiducial registration error (FRE) is utilized to evaluate accuracy of real-time navigation system. Results: We performed laparoscopic hepatectomy using this system in 21 patients. Mean FRE of initial 5 patients was 17.7mm. First improvement was that MDCT were taken using radiological markers for registration of body parts. Mean FRE of the 8 patients who utilized first improvement was 10.2mm and decreased (p = 0.014). Second improvement was that a micro magnetic sensor as an intraoperative body position sensor was fixed on the right-sided chest wall and meant that pre- and post- operative FRE was similar due to an intraoperatively automatic correction of gap of body position. Preoperative and postoperative mean FRE of the 8 patients who utilized second improvement were 11.1mm and 10.1mm (p = 0.250). Conclusions: Our real-time navigation system can assist laparoscopic hepatectomy.

Velocity‐Preserving Trajectory Compression Based on Retrace Point Detection

With the increasing development of GPS‐equipped mobile devices such as smart phones and vehicle navigation systems, the trajectories containing valuable spatiotemporal information are recorded. Typically, plenty of trajectory records are generated and stored, making the device memory suffer a heavy storage pressure. Thus, it is a vital issue to compress the trajectories. The trajectory semantics are usually ignored or reduced in traditional trajectory compression techniques. In addition, most of existing trajectory compression algorithms only concern the position errors rather than the velocity errors of trajectories. This paper proposes a velocity‐preserving trajectory compression algorithm based on retrace point detection (VPTC‐RP) that can compress a set of trajectories by removing unnecessary redundancy points, while the skeleton of these trajectories is maintained as much as possible. In VPTC‐RP, the retrace points and the velocity errors are taken to reflect the speeds and directions attached with the points. VPTC‐RP first determines the retrace points based on the changed movement directions, and then, the retrace points are extracted from the original trajectories. Especially, the retrace points are put in a buffer, and the subtrajectories in the buffer are compressed according to the measured velocity errors. Simulations are carried out on the Geolife trajectory dataset, and the simulation results indicate that VPTC‐RP can achieve a preferable tradeoff among the compression error, compression ratio, and running time.