Key Technologies in Low-cost Integrated Vehicle Navigation Systems
2013 (English)Doctoral thesis, monograph (Other academic)
Vehicle navigation systems incorporate on-board sensors/signal receivers and provide necessary positioning and guidance information for land, marine, airborne and space vehicles. Among different navigation solutions, the Global Positioning System (GPS) and an Inertial Navigation System (INS) are two basic navigation systems. Due to their complementary characters in many aspects, a GPS/INS integrated navigation system has been a hot research topic in recent decades. Both advantages and disadvantages of each individual system and their combination are analysed in this thesis.
The Micro Electrical Mechanical Sensors (MEMS) successfully solved the problems of price, size and weight with traditional INS, and hence are widely applied in GPS/INS integrated systems. The main problem of MEMS is the large sensor errors, which rapidly degrade the navigation performance in an exponential speed. By means of different methods, such as autoregressive model, Gauss-Markov process, Power Spectral Density and Allan Variance, we analyse the stochastic errors within the MEMS sensors. The test results show that different methods give similar estimates of stochastic error sources. An equivalent model of coloured noise components (random walk, bias instability and ramp noise) is given.
Three levels of GPS/IMU integration structures, i.e. loose, tight and ultra-tight GPS/IMU navigation, are introduced with a brief analysis of each character. The loose integration principles are presented with detailed equations as well as the INS navigation principles. The Extended Kalman Filter (EKF) is introduced as the data fusion algorithm, which is the core of the whole navigation system. Based on the system model, we show the propagation of position standard errors with the tight integration structure under different scenarios. Even less than 4 observable GNSS satellites can contribute to the integrated system, especially for the orientation errors. A real test with loose integration is carried out, and the EKF performance is analysed in detail.
Since the GPS receivers are normally working with a digital map, the map matching principle and its link-choosing problem are briefly introduced. This problem is proposed to be solved by the lane detection from real-time images. The procedures for the lane detection based on image processing are presented. The test on high ways, city streets and pathways are successfully carried out, and analyses with possible solutions are given for some special failure situations.
To solve the large error drift of the IMU, we propose to support the IMU orientation with camera motion estimation from image pairs. First the estimation theory and computer vision principles are briefly introduced. Then both point and line matches algorithms are given. Finally the L1-norm estimator with balanced adjustment is proposed to deal with possible mismatches (outliers). Tests and comparisons with the RANSAC algorithm are also presented.
For the latest trend of MEMS chip sensors, their industry and market are introduced. To evaluate the MEMS navigation performance, we augment the EKF with an equivalent coloured noise model, and the basic observability analysis is given. A realistic simulated navigation test is carried out with single and multiple MEMS sensors, and a sensor array of 5-10 sensors are recommended according to the test results and analysis. Finally some suggestions for future research are proposed.
Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2013. , xi, 181 p.
Trita-SOM , ISSN 1653-6126 ; 2013:11
GPS, INS, MEMS, error modelling, integration structure, lane detection, camera motion, balance adjustment, sensor array
Other Engineering and Technologies not elsewhere specified
IdentifiersURN: urn:nbn:se:kth:diva-131420ISBN: 978-91-7501-887-4OAI: oai:DiVA.org:kth-131420DiVA: diva2:656086
2013-11-08, E2, Lindstedsvägen 3, KTH, Stockholm, 13:00 (English)
Nahavandchi, Hossein, Professor
Sjöberg, Lars E., ProfessorHoremuž, Milan, Docent
QC 201310162013-10-162013-10-142013-10-17Bibliographically approved