15 Vehicle Ego-Localization with a Monocular Camera Using Epipolar Geometry Constraints
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Haruya Kyutoku
Abstract
Nowadays, the development of driving support systems and autonomous driving systems have become active. Vehicle ego-localization using in-vehicle sensors is one of the most important technologies for these systems. Accordingly, various attempts to localize own vehicle from in-vehicle sensors have been made. In general, the estimation accuracy of the traveling direction is lower than in the lateral direction. Therefore, we present a highly accurate method for ego-localization of the traveling direction based on epipolar geometry using an in-vehicle monocular camera. The presented method makes correspondences between in-vehicle camera images and database images with location information, and calculates the location using locations annotated to the corresponding database images. However, there are many gaps due to the difference in speed and trajectory of vehicles even if the images are obtained along the same road. To overcome this problem, the distance between the input image and the database image is calculated by the distance metric based on the epipolar geometry and the local feature method. An experiment was conducted using actual images with correct locations, and the effectiveness of the presented method was confirmed from its results.
Abstract
Nowadays, the development of driving support systems and autonomous driving systems have become active. Vehicle ego-localization using in-vehicle sensors is one of the most important technologies for these systems. Accordingly, various attempts to localize own vehicle from in-vehicle sensors have been made. In general, the estimation accuracy of the traveling direction is lower than in the lateral direction. Therefore, we present a highly accurate method for ego-localization of the traveling direction based on epipolar geometry using an in-vehicle monocular camera. The presented method makes correspondences between in-vehicle camera images and database images with location information, and calculates the location using locations annotated to the corresponding database images. However, there are many gaps due to the difference in speed and trajectory of vehicles even if the images are obtained along the same road. To overcome this problem, the distance between the input image and the database image is calculated by the distance metric based on the epipolar geometry and the local feature method. An experiment was conducted using actual images with correct locations, and the effectiveness of the presented method was confirmed from its results.
Chapters in this book
- Frontmatter I
- Contents V
- Contributing Authors VII
- Introduction XI
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Part A: Driver/Vehicle Interaction Systems
- 1 MobileUTDrive: A Portable Device Platform for In-vehicle Driving Data Collection 3
- 2 Semantic Analysis of Driver Behavior by Data Fusion 25
- 3 Predicting When Drivers Need AR Guidance 35
- 4 Driver’s Mental Workload Estimation with Involuntary Eye Movement 49
- 5 Neurophysiological Driver Behavior Analysis 67
- 6 Modeling the Relationship between Driver Gaze Behavior and Traffic Context during Lane Changes Using a Recurrent Neural Network 87
- 7 A Multimodal Control System for Autonomous Vehicles Using Speech, Gesture, and Gaze Recognition 101
- 8 Head Pose as an Indicator of Drivers’ Visual Attention 113
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Part B: Models & Theories of Driver/Vehicle Systems
- 9 Evolving Neural Network Controllers for Tractor-Trailer Vehicle Backward Path Tracking 135
- 10 Spectral Distance Analysis for Quality Estimation of In-Car Communication Systems 149
- 11 Combination of Hands-Free and ICC Systems 165
- 12 Insights into Automotive Noise PSD Estimation Based on Multiplicative Constants 183
- 13 In-Car Communication: From Single- to Four-Channel with the Frequency Domain Adaptive Kalman Filter 213
-
Part C: Self–driving and the Mobility in 2050
- 14 The PIX Moving KuaiKai: Building a Self-Driving Car in Seven Days 233
- 15 Vehicle Ego-Localization with a Monocular Camera Using Epipolar Geometry Constraints 251
- 16 Connected and Automated Vehicles: Study of Platooning 263
- 17 Epilogue – Future Mobility 2050 285
- Index 311
Chapters in this book
- Frontmatter I
- Contents V
- Contributing Authors VII
- Introduction XI
-
Part A: Driver/Vehicle Interaction Systems
- 1 MobileUTDrive: A Portable Device Platform for In-vehicle Driving Data Collection 3
- 2 Semantic Analysis of Driver Behavior by Data Fusion 25
- 3 Predicting When Drivers Need AR Guidance 35
- 4 Driver’s Mental Workload Estimation with Involuntary Eye Movement 49
- 5 Neurophysiological Driver Behavior Analysis 67
- 6 Modeling the Relationship between Driver Gaze Behavior and Traffic Context during Lane Changes Using a Recurrent Neural Network 87
- 7 A Multimodal Control System for Autonomous Vehicles Using Speech, Gesture, and Gaze Recognition 101
- 8 Head Pose as an Indicator of Drivers’ Visual Attention 113
-
Part B: Models & Theories of Driver/Vehicle Systems
- 9 Evolving Neural Network Controllers for Tractor-Trailer Vehicle Backward Path Tracking 135
- 10 Spectral Distance Analysis for Quality Estimation of In-Car Communication Systems 149
- 11 Combination of Hands-Free and ICC Systems 165
- 12 Insights into Automotive Noise PSD Estimation Based on Multiplicative Constants 183
- 13 In-Car Communication: From Single- to Four-Channel with the Frequency Domain Adaptive Kalman Filter 213
-
Part C: Self–driving and the Mobility in 2050
- 14 The PIX Moving KuaiKai: Building a Self-Driving Car in Seven Days 233
- 15 Vehicle Ego-Localization with a Monocular Camera Using Epipolar Geometry Constraints 251
- 16 Connected and Automated Vehicles: Study of Platooning 263
- 17 Epilogue – Future Mobility 2050 285
- Index 311
