Monocular Visual Odometry Based on Hybrid Parameterization

EasyChair Preprint no. 1513

Abstract

Visual odometry (VO) is one of the most challenging techniques in computer vision for autonomous vehicle/vessels. In VO, the camera pose that also represents the robot pose in ego-motion is estimated analyzing the features and pixels extracted from the camera images. Different VO techniques mainly provide different trade-offs among the resources that are being considered for odometry, such as camera resolution, computation/communication capacity, power/energy consumption, and accuracy. In this paper, a hybrid technique is proposed for camera pose estimation by combining odometry based on triangulation using the long-term period of direct-based odometry and the short-term period of inverse depth mapping. Experimental results based on the EuRoC data set shows that the proposed technique significantly outperforms the traditional direct-based pose estimation method for Micro Aerial Vehicle (MAV), keeping its potential negative effect on performance negligible.

Keyphrases: Inverse-depth map, monocular camera, Visual Oodmetry

@Booklet{EasyChair:1513,
  author = {Sherif Mohamed and Mohammad-Hashem Haghbayan and Jukka Heikkonen and Hannu Tenhunen and Juha Plosila},
  title = {Monocular Visual Odometry Based on Hybrid Parameterization},
  howpublished = {EasyChair Preprint no. 1513},

  year = {EasyChair, 2019}}