Review and analysis of solutions of the three point perspective pose estimation problem
International Journal of Computer Vision
A Space-Sweep Approach to True Multi-Image Matching
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
An Efficient Solution to the Five-Point Relative Pose Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
Detailed Real-Time Urban 3D Reconstruction from Video
International Journal of Computer Vision
Automatic Generator of Minimal Problem Solvers
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Real-time monocular visual odometry for on-road vehicles with 1-point RANSAC
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
P2Π: a minimal solution for registration of 3D points to 3D planes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Multi-resolution real-time stereo on commodity graphics hardware
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Performance evaluation of 1-point-RANSAC visual odometry
Journal of Field Robotics
DTAM: Dense tracking and mapping in real-time
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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More and more on-road vehicles are equipped with cameras each day. This paper presents a novel method for estimating the relative motion of a vehicle from a sequence of images obtained using a single vehicle-mounted camera. Recently, several researchers in robotics and computer vision have studied the performance of motion estimation algorithms under non-holonomic constraints and planarity. The successful algorithms typically use the smallest number of feature correspondences with respect to the motion model. It has been strongly established that such minimal algorithms are efficient and robust to outliers when used in a hypothesize-and-test framework such as random sample consensus (RANSAC). In this paper, we show that the planar 2-point motion estimation can be solved analytically using a single quadratic equation, without the need of iterative techniques such as Newton-Raphson method used in existing work. Non-iterative methods are more efficient and do not suffer from local minima problems. Although 2-point motion estimation generates visually accurate on-road vehicle trajectory, the motion is not precise enough to perform dense 3D reconstruction due to the non-planarity of roads. Thus we use a 2-point relative motion algorithm for the initial images followed by 3-point 2D-to-3D camera pose estimation for the subsequent images. Using this hybrid approach, we generate accurate motion estimates for a plane-sweeping algorithm that produces dense depth maps for obstacle detection applications.