Motion from point matches: multiple of solutions
International Journal of Computer Vision
Exact Two-Image Structure from Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Obstacle avoidance and navigation in the real world by a seeing robot rover
Obstacle avoidance and navigation in the real world by a seeing robot rover
Real-Time Simultaneous Localisation and Mapping with a Single Camera
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Preemptive RANSAC for live structure and motion estimation
Machine Vision and Applications
Bearings-only localization and mapping
Bearings-only localization and mapping
Two years of Visual Odometry on the Mars Exploration Rovers: Field Reports
Journal of Field Robotics - Special Issue on Space Robotics
Introduction to Autonomous Mobile Robots
Introduction to Autonomous Mobile Robots
Appearance-Guided Monocular Omnidirectional Visual Odometry for Outdoor Ground Vehicles
IEEE Transactions on Robotics
Combining edge and one-point RANSAC algorithm to estimate visual odometry
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
Cleaning robot navigation using panoramic views and particle clouds as landmarks
Robotics and Autonomous Systems
Hi-index | 0.00 |
This paper presents a new method to estimate the relative motion of a vehicle from images of a single camera. The computational cost of the algorithm is limited only by the feature extraction and matching process, as the outlier removal and the motion estimation steps take less than a fraction of millisecond with a normal laptop computer. The biggest problem in visual motion estimation is data association; matched points contain many outliers that must be detected and removed for the motion to be accurately estimated. In the last few years, a very established method for removing outliers has been the "5-point RANSAC" algorithm which needs a minimum of 5 point correspondences to estimate the model hypotheses. Because of this, however, it can require up to several hundreds of iterations to find a set of points free of outliers. In this paper, we show that by exploiting the nonholonomic constraints of wheeled vehicles it is possible to use a restrictive motion model which allows us to parameterize the motion with only 1 point correspondence. Using a single feature correspondence for motion estimation is the lowest model parameterization possible and results in the two most efficient algorithms for removing outliers: 1-point RANSAC and histogram voting. To support our method we run many experiments on both synthetic and real data and compare the performance with a state-of-the-art approach. Finally, we show an application of our method to visual odometry by recovering a 3 Km trajectory in a cluttered urban environment and in real-time.