Navigation Aiding Based on Coupled Online Mosaicking and Camera Scanning

Abstract

This paper presents a new method for vision-aided navigation of airborne platforms. The method is based on online mosaicking using images acquired by an on-board gimballed camera, which scans ground regions in the vicinity of the flight trajectory. The coupling of the scanning and mosaicking processes improves image-based motion estimation when operating in challenging scenarios such as narrow field-of-view cameras observing low-texture scenes. These improved motion estimations are fused with an inertial navigation system. The mosaic used for navigation is constructed in two levels. A small mosaic based on recently captured images is computed in real-time, and a larger mosaic including all the images is computed in a background process. The low-level mosaic is used for immediate motion estimation, while the higher-level mosaic is used for global navigation. The correlation terms between the navigation system and the mosaic construction process are not maintained in the proposed approach. The advantage of this architecture is the low computational load required for navigation aiding. However, the accuracy of the proposed method could be compromised compared to bearing-only Simultaneous Localization and Mapping (SLAM). The new method was examined using statistical simulation runs and experiments based on Google Earth imagery, showing its superior performance compared to traditional methods for two-view navigation aiding.

Vision-aided localization and navigation based on trifocal tensor geometry

“…In Soatto et al, 9 epipolar geometry is employed in conjunction with a statistical motion model, while in Prazenica et al, 10 epipolar constraints are fused with a dynamical model of an airplane. In Indelman et al, 11,12 the constraints between the current and previous image are defined using epipolar geometry and combined with IMU measurements in an extended Kalman filter (EKF). With two-view based methods for aiding navigation, however, it is only possible to determine camera rotations and up-to-scale translations 13 (translations during the intervals are associated with an unknown scale).…”

“…Assuming the camera calibration matrix is known, then the camera projection matrices can be defined as follows 11 where A=[a1,a2,a3]=Cc1 c2 is the rotation matrix from camera c 1 to camera c 2 , B=[b1,b2,b3]=Cc1 c3 is the rotation matrix from camera c 1 to camera c 3 , a4=T12 c2 is the translation expressed in camera c 2 from c 1 camera to camera c 2 , and b4=T13 c3 is the translation expressed in camera c 3 from camera c 1 to camera c 3 .…”

“…The model in equation (13) contains nine trilinearites, but only four are linearly independent. Geometrically, these four trilinearites 11 arise from special line choices in the second and third images for the point–line–line relation.…”

UKF for Integrated Vision and Inertial Sensors Based on Three-View Geometry

“…The first 9 components of X are given in the LLLN coordinates, while the last 6 are represented in the body-fixed reference frame. Then the continuous-time system process model is given by [2], [3]…”

“…Considering two overlapping images, it is only possible to determine camera rotation and up-to-scale translation [1]. Therefore, two-view based methods for navigation aiding [2]- [4] are incapable of eliminating the developing navigation errors in all states.…”