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Visual Odometry with an Event Camera Using Continuous Ray Warping and Volumetric Contrast Maximization

Yifu Wang, Jiaqi Yang, Xin Peng, Peng Wu, Ling Gao, Kun Huang, Jiaben Chen, Laurent Kneip

2022Sensors31 citationsDOIOpen Access PDF

Abstract

We present a new solution to tracking and mapping with an event camera. The motion of the camera contains both rotation and translation displacements in the plane, and the displacements happen in an arbitrarily structured environment. As a result, the image matching may no longer be represented by a low-dimensional homographic warping, thus complicating an application of the commonly used Image of Warped Events (IWE). We introduce a new solution to this problem by performing contrast maximization in 3D. The 3D location of the rays cast for each event is smoothly varied as a function of a continuous-time motion parametrization, and the optimal parameters are found by maximizing the contrast in a volumetric ray density field. Our method thus performs joint optimization over motion and structure. The practical validity of our approach is supported by an application to AGV motion estimation and 3D reconstruction with a single vehicle-mounted event camera. The method approaches the performance obtained with regular cameras and eventually outperforms in challenging visual conditions.

Topics & Concepts

Image warpingComputer visionArtificial intelligenceComputer scienceTranslation (biology)Rotation (mathematics)Event (particle physics)Visual odometryTracking (education)Image planeContrast (vision)Dynamic time warpingImage (mathematics)RobotPhysicsPsychologyBiochemistryMessenger RNAQuantum mechanicsGeneChemistryPedagogyAdvanced Memory and Neural ComputingRobotics and Sensor-Based LocalizationAdvanced Neural Network Applications
Visual Odometry with an Event Camera Using Continuous Ray Warping and Volumetric Contrast Maximization | Litcius