Event-Based Real-Time Moving Object Detection Based on IMU Ego-Motion Compensation

Accurate and timely onboard perception is a prerequisite for mobile robots to operate in highly dynamic scenarios. The bio-inspired event camera can capture more motion details than a traditional camera by triggering each pixel asynchronously and therefore is more suitable in such scenarios. Among various perception tasks based on the event camera, ego-motion removal is one fundamental procedure to reduce perception ambiguities. Recent ego-motion removal methods are mainly based on optimization processes and may be computationally expensive for robot applications. In this paper, we consider the challenging perception task of detecting fast-moving objects from an aggressively operated platform equipped with an event camera, achieving computational cost reduction by directly employing IMU motion measurement. First, we design a nonlinear warping function to capture rotation information from an IMU and to compensate for the camera motion during an asynchronous events stream. The pro- posed nonlinear warping function improves the compensation accuracy by 10%-15%. Afterward, we segmented the moving parts on the warped image through dynamic threshold segmen- tation and optical flow calculation, and clustering. Finally, we validate the proposed detection pipeline on public datasets and real-world data streams containing challenging light conditions and fast-moving objects.