TY - JOUR

T1 - Camera Pose Estimation Based on PnL With a Known Vertical Direction

AU - Lecrosnier, Louis

AU - Boutteau, Rémi

AU - Vasseur, Pascal

AU - Savatier, Xavier

AU - Fraundorfer, Friedrich

PY - 2019

Y1 - 2019

N2 - In this letter, we address the problem of camera pose estimation using two-dimensional (2D) and 3-D line features, also known as PnL (Perspective-n-Line) with a known vertical direction. The minimal number of line correspondences required to estimate the complete camera pose is 3 (P3L) in the general case, yielding to a minimum of eight possible solutions. Prior knowledge of the vertical direction, such as provided by common sensors (e.g., inertial measurement unit, or IMU), reduces the problem to a 4 DoF problem and outputs a single solution. We benefit this fact to decouple the remaining rotation estimation and the translation estimation and we present a twofold contribution: First, we present a linear formulation of the PnL problem in Plücker lines coordinates with a known vertical direction, including a Gauss–Newton-based orientation and location refinement to compensate IMU sensor noise. Second, we …

AB - In this letter, we address the problem of camera pose estimation using two-dimensional (2D) and 3-D line features, also known as PnL (Perspective-n-Line) with a known vertical direction. The minimal number of line correspondences required to estimate the complete camera pose is 3 (P3L) in the general case, yielding to a minimum of eight possible solutions. Prior knowledge of the vertical direction, such as provided by common sensors (e.g., inertial measurement unit, or IMU), reduces the problem to a 4 DoF problem and outputs a single solution. We benefit this fact to decouple the remaining rotation estimation and the translation estimation and we present a twofold contribution: First, we present a linear formulation of the PnL problem in Plücker lines coordinates with a known vertical direction, including a Gauss–Newton-based orientation and location refinement to compensate IMU sensor noise. Second, we …

U2 - 10.1109/LRA.2019.2929982

DO - 10.1109/LRA.2019.2929982

M3 - Article

SN - 2377-3766

VL - 4

SP - 3852

EP - 3859

JO - IEEE Robotics and Automation Letters

JF - IEEE Robotics and Automation Letters

IS - 4

ER -