TY - GEN
T1 - A Visual Inertial Odometry Framework for 3D Points, Lines and Planes
AU - Kannapiran, Shenbagaraj
AU - Van Baar, Jeroen
AU - Berman, Spring
N1 - Funding Information:
†Work partially done during an internship at Mitsubishi Electric Research Laboratories (MERL). This work was supported in part by the Arizona State University Global Security Initiative.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Recovering rigid registration between successive camera poses lies at the heart of 3D reconstruction, SLAM and visual odometry. Registration relies on the ability to compute discriminative 2D features in successive camera images for determining feature correspondences, which is very challenging in feature-poor environments, i.e. low-texture and/or low-light environments. In this paper, we aim to address the challenge of recovering rigid registration between successive camera poses in feature-poor environments in a Visual Inertial Odometry (VIO) setting. In addition to inertial sensing, we instrument a small aerial robot with an RGBD camera and propose a framework that unifies the incorporation of 3D geometric entities: points, lines, and planes. The tracked 3D geometric entities provide constraints in an Extended Kalman Filtering framework. We show that by directly exploiting 3D geometric entities, we can achieve improved registration. We demonstrate our approach on different texture-poor environments, with some containing only flat texture-less surfaces providing essentially no 2D features for tracking. In addition, we evaluate how the addition of different 3D geometric entities contributes to improved pose estimation by comparing an estimated pose trajectory to a ground truth pose trajectory obtained from a motion capture system. We consider computationally efficient methods for detecting 3D points, lines and planes, since our goal is to implement our approach on small mobile robots, such as drones.
AB - Recovering rigid registration between successive camera poses lies at the heart of 3D reconstruction, SLAM and visual odometry. Registration relies on the ability to compute discriminative 2D features in successive camera images for determining feature correspondences, which is very challenging in feature-poor environments, i.e. low-texture and/or low-light environments. In this paper, we aim to address the challenge of recovering rigid registration between successive camera poses in feature-poor environments in a Visual Inertial Odometry (VIO) setting. In addition to inertial sensing, we instrument a small aerial robot with an RGBD camera and propose a framework that unifies the incorporation of 3D geometric entities: points, lines, and planes. The tracked 3D geometric entities provide constraints in an Extended Kalman Filtering framework. We show that by directly exploiting 3D geometric entities, we can achieve improved registration. We demonstrate our approach on different texture-poor environments, with some containing only flat texture-less surfaces providing essentially no 2D features for tracking. In addition, we evaluate how the addition of different 3D geometric entities contributes to improved pose estimation by comparing an estimated pose trajectory to a ground truth pose trajectory obtained from a motion capture system. We consider computationally efficient methods for detecting 3D points, lines and planes, since our goal is to implement our approach on small mobile robots, such as drones.
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U2 - 10.1109/IROS51168.2021.9636526
DO - 10.1109/IROS51168.2021.9636526
M3 - Conference contribution
AN - SCOPUS:85124358842
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 9206
EP - 9211
BT - IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
Y2 - 27 September 2021 through 1 October 2021
ER -