Vision-aided inertial navigation with line features and a rolling-shutter camera

The vast majority of existing methods for vision-aided inertial navigation rely on the detection and tracking of point features in the images. However, in several man-made environments, such as indoor office spaces, straight line features are prevalent, while point features may be sparse. Therefore, developing methods that will enable the use of straight-line features for vision-aided inertial navigation can lead to improved performance. While limited prior work on the subject exists, it assumes the use of a global-shutter camera, i.e., a camera in which all image pixels are captured simultaneously. Most low-cost cameras, however, use rolling-shutter (RS) image capture, which renders the existing methods inapplicable. To address these limitations, we here present an algorithm for vision-aided inertial navigation that employs both point and line features, and is capable of operation with RS cameras. The two key contributions of this work are (i) a novel parameterization for 3D lines, which is shown to exhibit better linearity properties than existing ones, and (ii) a novel approach for the use of line observations in images. This approach forgoes line-fitting and does not assume that a straight line in 3D projects to a straight line in the image, and is thus suitable for use with RS cameras. Our results demonstrate that our proposed estimator formulation leads to improved precision, in point-feature-poor environments.