Image-Based Ego-Motion Estimation Using On-Vehicle Omnidirectional Camera

The estimation of the motion of the sensor, as well as a 3D shape of a scene, has been extensively researched, especially for Virtual Reality (VR) and Robotics systems. To achieve this estimation, a system that consists of a laser range sensor, Global Positioning System (GPS), and Gyro sensor has been proposed, actually constructed, and used. However, it is usually difficult to produce a precise and detailed estimation of the 3D shape because of the limited ability of each sensor. The Structure from Motion (SfM) method is widely known for estimation purposes, and the method can estimate those parameters in pixel order. However, the SfM method is frequently unstable because of dependency on initial parameters and also because of noise. In this paper, we propose a SfM method for omnidirectional image sequences using both factorization and a bundle adjustment method to achieve high accuracy and robustness.