Globally-Optimal Event Camera Motion Estimation

Abstract

Event cameras are bio-inspired sensors that perform well in HDR conditions and have high temporal resolution. However, different from traditional frame-based cameras, event cameras measure asynchronous pixel-level brightness changes and return them in a highly discretised format, hence new algorithms are needed. The present paper looks at fronto-parallel motion estimation of an event camera. The flow of the events is modeled by a general homographic warping in a space-time volume, and the objective is formulated as a maximisation of contrast within the image of unwarped events. However, in stark contrast to prior art, we derive a globally optimal solution to this generally non-convex problem, and thus remove the dependency on a good initial guess. Our algorithm relies on branch-and-bound optimisation for which we derive novel, recursive upper and lower bounds for six different contrast estimation functions. The practical validity of our approach is supported by a highly successful application to AGV motion estimation with a downward facing event camera, a challenging scenario in which the sensor experiences fronto-parallel motion in front of noisy, fast moving textures.

References in corpus (8)

• Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
• EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras
• Event-based Moving Object Detection and Tracking
• A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation
• Event-Based Motion Segmentation by Motion Compensation
• Efficient Globally-Optimal Correspondence-Less Visual Odometry for Planar Ground Vehicles
• Improved Fourier Mellin Invariant for Robust Rotation Estimation with Omni-cameras
• Simultaneous Optical Flow and Segmentation (SOFAS) using Dynamic Vision Sensor

Cited by in corpus (8)

• CMax-SLAM: Event-based Rotational-Motion Bundle Adjustment and SLAM System using Contrast Maximization
• Globally-Optimal Contrast Maximisation for Event Cameras
• The Spatio-Temporal Poisson Point Process: A Simple Model for the Alignment of Event Camera Data
• A 5-Point Minimal Solver for Event Camera Relative Motion Estimation
• Accelerating Globally Optimal Consensus Maximization in Geometric Vision
• Comparing Representations in Tracking for Event Camera-based SLAM
• Spatiotemporal Registration for Event-based Visual Odometry
• Dynamic Event Camera Calibration