Vision for mobile robot localization in urban environments

This paper addresses the problem of mobile robot localization in urban environments. Typically, GPS is the preferred sensor for outdoor operation. However, using GPS-only localization methods leads to significant performance degradation in urban areas where tall nearby structures obstruct the clear view of the satellites. In our work, we use vision-based techniques to supplement GPS and odometry and provide accurate localization. The vision system identifies prominent linear features in the scene and matches them with a reduced model of nearby buildings, yielding improved pose estimation of the robot

Towards autonomous localization and mapping of AUVs: a survey

Purpose The main purpose of this paper is to investigate two key elements of localization and mapping of Autonomous Underwater Vehicle (AUV), i.e. to overview various sensors and algorithms used for underwater localization and mapping, and to make suggestions for future research. Design/methodology/approach The authors first review various sensors and algorithms used for AUVs in the terms of basic working principle, characters, their advantages and disadvantages. The statistical analysis is carried out by studying 35 AUV platforms according to the application circumstances of sensors and algorithms. Findings As real-world applications have different requirements and specifications, it is necessary to select the most appropriate one by balancing various factors such as accuracy, cost, size, etc. Although highly accurate localization and mapping in an underwater environment is very difficult, more and more accurate and robust navigation solutions will be achieved with the development of both sensors and algorithms. Research limitations/implications This paper provides an overview of the state of art underwater localisation and mapping algorithms and systems. No experiments are conducted for verification. Practical implications The paper will give readers a clear guideline to find suitable underwater localisation and mapping algorithms and systems for their practical applications in hand. Social implications There is a wide range of audiences who will benefit from reading this comprehensive survey of autonomous localisation and mapping of UAVs. Originality/value The paper will provide useful information and suggestions to research students, engineers and scientists who work in the field of autonomous underwater vehicles

A Study on Underwater Navigation System of Sensor Model-based Underwater Track Robot

In this study, an underwater navigation algorithm was developed to apply the underwater navigation system to the underwater track robot. Generally, underwater navigation uses a Doppler Velocity Log(DVL) to measure the velocity of underwater vehicles. However, undersea platforms, such as underwater track robots, cannot use DVL due to the distance limitations of sensor operation. As a result, Dead Reckoning(DR) navigation is inevitably used, and which results in severe errors in attitude and position values over long periods of platform operation. To overcome this problem, we developed an underwater navigation system composed of coupled Inertial Navigation System(INS) composed Ultra Short Base Line(USBL) and additional track information. The INS sensors were modeled using the mathematical model of the accelerometer, the gyroscope, the magnetometer. Before the experiment, computer simulations were performed to analyze the expected sensor values for specific track missions in unexpected situations. Based on this, we developed an underwater navigation algorithm for a prototype underwater track robot which we developed at the lab and confirmed the effectiveness of the navigation algorithm through experiments. For the prototype underwater track robot, we developed the navigation system, the electric hardware, the control system, and the operating system. Finally, we applied the developed INS and the underwater navigation algorithm to the platform and verified a good performance through real sea experiments.1. 서 론 1.1 연구 배경 1 1.1.1 수중트랙로봇 2 1.1.2 항법 5 1.2 연구 목표 7 1.3 논문 구성 8 2. 항법 센서의 수학적 모델 2.1 관성 측정 장치 9 2.1.1 가속도계 10 2.1.2 각속도계 15 2.1.3 자력계 20 3. 융합 항법 알고리즘 3.1 좌표계 25 3.2 INS 항법 알고리즘 설계 26 3.2.1 자세 추정 26 3.2.2 속도 추정 33 3.2.3 위치 추정 34 3.2.4 INS 오차 모델 35 3.3 융합 항법 알고리즘 설계 38 3.3.1 시스템 오차 모델 38 3.3.2 측정 오차 모델 41 3.4 융합 항법 알고리즘 구성 41 3.5 융합 항법 시뮬레이션 42 3.5.1 S자 궤적 항법 시뮬레이션 44 3.5.2 사각형 궤적 항법 시뮬레이션 50 3.5.3 모의 진회수 항법 시뮬레이션 56 4. 수중항법 시스템 및 플랫폼 구성 4.1 수중항법 시스템 63 4.1.1 시스템 구성 63 4.1.2 운용 시스템 67 4.2 수중트랙로봇 69 4.2.1 플랫폼 구성 69 4.2.2 운용 시스템 76 5. 융합 항법 성능 실험 5.1 트랜칭 수중트랙로봇에 적용한 융합 항법 성능 실험 77 5.1.1 Heading 추정 성능 실험 79 5.1.2 실해역 융합 항법 성능 실험 80 5.2 수중트랙로봇에 적용한 융합 항법 성능 실험 86 5.2.1 기본 항법 성능 실험 88 5.2.2 미션 궤적 항법 성능 실험 91 5.2.3 사각형 궤적 항법 성능 실험 #1 95 5.2.4 사각형 궤적 항법 성능 실험 #2 98 6. 결론 103 부록 106 참고문헌 114 감사의 글 119Docto

Estudi per a la fusió de dades de posició i actitud en un multirotor AscTec Hummingbird

L'objectiu principal és aconseguir una estimació acurada de l'estat d'un vehicle aeri no tripulat, un quadrirotor AscTec Hummingbird. S'entén per estat el valor de les variables que descriuen la postura del vehicle: angles, posicions i principals derivades.Hi ha dos objectius derivats: integrar un dispositiu de mesura de posició, mitjançant GPS amb Real Time Kinematic, Ashtech MB100 a bord del UAV; i dissenyar i programar un algoritme de fusió de dades embarcat a l'aeronau.Estudi dels protocols de comunicació de dispositius GPS convencionalsEstudi del software de control del Hummingbird, apartat de lectura de sensorsIntegració del MB100 i modificació del software si convéEstudi del software de control del Hummingbird, apartat de fusió de dadesEstudi del mètodes de fusió de dades (observadors de Luenberger, filtre de Kalman, filtre estés de Kalman)Integració de les noves dades i modificació del softwares si conv

Task-Driven Integrity Assessment and Control for Vehicular Hybrid Localization Systems

Throughout the last decade, vehicle localization has been attracting significant attention in a wide range of applications, including Navigation Systems, Road Tolling, Smart Parking, and Collision Avoidance. To deliver on their requirements, these applications need specific localization accuracy. However, current localization techniques lack the required accuracy, especially for mission critical applications. Although various approaches for improving localization accuracy have been reported in the literature, there is still a need for more efficient and more effective measures that can ascribe some level of accuracy to the localization process. These measures will enable localization systems to manage the localization process and resources so as to achieve the highest accuracy possible, and to mitigate the impact of inadequate accuracy on the target application. In this thesis, a framework for fusing different localization techniques is introduced in order to estimate the location of a vehicle along with location integrity assessment that captures the impact of the measurement conditions on the localization quality. Knowledge about estimate integrity allows the system to plan the use of its localization resources so as to match the target accuracy of the application. The framework introduced provides the tools that would allow for modeling the impact of the operation conditions on estimate accuracy and integrity, as such it enables more robust system performance in three steps. First, localization system parameters are utilized to contrive a feature space that constitutes probable accuracy classes. Due to the strong overlap among accuracy classes in the feature space, a hierarchical classification strategy is developed to address the class ambiguity problem via the class unfolding approach (HCCU). HCCU strategy is proven to be superior with respect to other hierarchical configuration. Furthermore, a Context Based Accuracy Classification (CBAC) algorithm is introduced to enhance the performance of the classification process. In this algorithm, knowledge about the surrounding environment is utilized to optimize classification performance as a function of the observation conditions. Second, a task-driven integrity (TDI) model is developed to enable the applications modules to be aware of the trust level of the localization output. Typically, this trust level functions in the measurement conditions; therefore, the TDI model monitors specific parameter(s) in the localization technique and, accordingly, infers the impact of the change in the environmental conditions on the quality of the localization process. A generalized TDI solution is also introduced to handle the cases where sufficient information about the sensing parameters is unavailable. Finally, the produce of the employed localization techniques (i.e., location estimates, accuracy, and integrity level assessment) needs to be fused. Nevertheless, these techniques are hybrid and their pieces of information are conflicting in many situations. Therefore, a novel evidence structure model called Spatial Evidence Structure Model (SESM) is developed and used in constructing a frame of discernment comprising discretized spatial data. SESM-based fusion paradigms are capable of performing a fusion process using the information provided by the techniques employed. Both the location estimate accuracy and aggregated integrity resultant from the fusion process demonstrate superiority over the employing localization techniques. Furthermore, a context aware task-driven resource allocation mechanism is developed to manage the fusion process. The main objective of this mechanism is to optimize the usage of system resources and achieve a task-driven performance. Extensive experimental work is conducted on real-life and simulated data to validate models developed in this thesis. It is evident from the experimental results that task-driven integrity assessment and control is applicable and effective on hybrid localization systems