An Event-based Algorithm for Simultaneous 6-DOF Camera Pose Tracking and Mapping

TL;DR

This paper introduces an event-based SLAM algorithm that processes asynchronous data from event cameras to simultaneously estimate 6-DOF camera pose and reconstruct scenes, including an inertial sensor integration for improved accuracy.

Contribution

It adapts image-based SLAM techniques to event cameras and proposes an inertial extension, demonstrating competitive performance on public datasets.

Findings

The algorithm produces accurate 6-DOF pose estimates.

Event-inertial pipeline achieves comparable or better accuracy than state-of-the-art.

The method effectively reconstructs scenes from event data.

Abstract

Compared to regular cameras, Dynamic Vision Sensors or Event Cameras can output compact visual data based on a change in the intensity in each pixel location asynchronously. In this paper, we study the application of current image-based SLAM techniques to these novel sensors. To this end, the information in adaptively selected event windows is processed to form motion-compensated images. These images are then used to reconstruct the scene and estimate the 6-DOF pose of the camera. We also propose an inertial version of the event-only pipeline to assess its capabilities. We compare the results of different configurations of the proposed algorithm against the ground truth for sequences of two publicly available event datasets. We also compare the results of the proposed event-inertial pipeline with the state-of-the-art and show it can produce comparable or more accurate results provided the map estimate is reliable.