Research Group on Visual Computation

Description

Pose estimation is a fundamental building block of various vision applications, e.g. visual odometry, image based localization and navigation, fusion, and augmented reality. Herein, we are interested in absolute pose estimation, which consists of determining the position and orientation of a camera with respect to a 3D world coordinate frame. We propose a novel solution to the gPnL problem with known vertical direction. The vertical direction is typically obtained from an IMU (Inertial Measurement Unit). The only assumption about our generalized camera is that projection rays of a 3D line fall into coplanar subsets yielding a pencil of projection planes. Important special cases of such a camera include stereo and multiview perspective camera systems, perspective camera moving along a trajectory, as well as other non-perspective cameras with central omnidirectional or orthographic projection.

Video 1. Presentation of the extracted lines which we used for our algorithm as a least squares solver to estimate the position of the camera system. The corresponding 3D lines are shown with the same color as the camera.

For the quantitative evaluation of our generalized pose estimation algorithm with line correspondences, we generated various benchmark datasets of 3D-2D line pairs. Each dataset has 1000 samples. Our method can be used as a least square solver as well as a minimal solver without reformulation. For the minimal case we only need 3 line pairs. The method was quantitatively evaluated on this large synthetic dataset as well as on a real dataset, which confirms its state of the art performance both in terms of quality and computational efficiency.

Results

Video 2. Lidar laser scan for testing our pose estimation algorithms with a 3-perspective-1-omnidirectional multi-view camera system. On the Lidar scan red dots are the estimated positions and green dots are the real location of the markers in metric 3D space.

Video 3. Lidar laser scan for testing our pose estimation algorithms solver with a 3-perspective camera system. On the Lidar scan, red dots are the estimated positions of our minimal solver, blue dots are the estimated positions of NP3L [G.H.Lee, ECCV 2016], and green dots are the real location of the markers in the 3D metric space.