Abstract
Event-based vision sensors show great promise for use in embedded applications requiring low-latency passive sensing at a low computational cost. In this paper, we present an event-based algorithm that relies on an Extended Kalman Filter for 6-Degree of Freedom sensor pose estimation. The algorithm updates the sensor pose event-by-event with low latency (worst case of less than 2 μs on an FPGA). Using a single handheld sensor, we test the algorithm on multiple recordings, ranging from a high contrast printed planar scene to a more natural scene consisting of objects viewed from above. The pose is accurately estimated under rapid motions, up to 2.7 m/s. Thereafter, an extension to multiple sensors is described and tested, highlighting the improved performance of such a setup, as well as the integration with an off-the-shelf mapping algorithm to allow point cloud updates with a 3D scene and enhance the potential applications of this visual odometry solution.
Highlights
Six Degree of Freedom (6-DoF) poses information that is useful in many applications, most notably in the emerging fields of Augmented Reality and Virtual Reality (AR/VR), and in autonomous mobile vehicles
Full 6-DoF pose information is especially useful for aerial vehicles whose motion is far less constrained than ground vehicles
We present a real-time approach for Event-based Sensor Pose Estimation using an Extended Kalman Filter (ESPEE)
Summary
Six Degree of Freedom (6-DoF) poses information that is useful in many applications, most notably in the emerging fields of Augmented Reality and Virtual Reality (AR/VR), and in autonomous mobile vehicles. In the realm of mobile vehicles, knowledge of a vehicle’s 6-DoF pose is required in motion control [2], as well as in Simultaneous Localization and Mapping (SLAM) algorithms [3,4]. Full 6-DoF pose information is especially useful for aerial vehicles whose motion is far less constrained than ground vehicles. An ideal 6-DoF pose estimation sensor would not rely on any pre-installed infrastructure in the environment. The sensor would be a self-contained, embedded system suitable for deployment in unknown environments. Pose estimation typically requires the sensor to have either a prior or acquired knowledge of its environment. Visual odometry is a possible passive sensing approach for pose estimation [5]
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