Event-Based Robotic Grasping Detection With Neuromorphic Vision Sensor and Event-Grasping Dataset.

Robotic grasping plays an important role in the field of robotics. The current state-of-the-art robotic grasping detection systems are usually built on the conventional vision, such as the RGB-D camera. Compared to traditional frame-based computer vision, neuromorphic vision is a small and young community of research. Currently, there are limited event-based datasets due to the troublesome annotation of the asynchronous event stream. Annotating large scale vision datasets often takes lots of computation resources, especially when it comes to troublesome data for video-level annotation. In this work, we consider the problem of detecting robotic grasps in a moving camera view of a scene containing objects. To obtain more agile robotic perception, a neuromorphic vision sensor (Dynamic and Active-pixel Vision Sensor, DAVIS) attaching to the robot gripper is introduced to explore the potential usage in grasping detection. We construct a robotic grasping dataset named Event-Grasping dataset with 91 objects. A spatial-temporal mixed particle filter (SMP Filter) is proposed to track the LED-based grasp rectangles, which enables video-level annotation of a single grasp rectangle per object. As LEDs blink at high frequency, the Event-Grasping dataset is annotated at a high frequency of 1 kHz. Based on the Event-Grasping dataset, we develop a deep neural network for grasping detection that considers the angle learning problem as classification instead of regression. The method performs high detection accuracy on our Event-Grasping dataset with 93% precision at an object-wise level split. This work provides a large-scale and well-annotated dataset and promotes the neuromorphic vision applications in agile robot.

Multi-Cue Event Information Fusion for Pedestrian Detection With Neuromorphic Vision Sensors.

Neuromorphic vision sensors are bio-inspired cameras that naturally capture the dynamics of a scene with ultra-low latency, filtering out redundant information with low power consumption. Few works are addressing the object detection with this sensor. In this work, we propose to develop pedestrian detectors that unlock the potential of the event data by leveraging multi-cue information and different fusion strategies. To make the best out of the event data, we introduce three different event-stream encoding methods based on Frequency, Surface of Active Event (SAE) and Leaky Integrate-and-Fire (LIF). We further integrate them into the state-of-the-art neural network architectures with two fusion approaches: the channel-level fusion of the raw feature space and decision-level fusion with the probability assignments. We present a qualitative and quantitative explanation why different encoding methods are chosen to evaluate the pedestrian detection and which method performs the best. We demonstrate the advantages of the decision-level fusion via leveraging multi-cue event information and show that our approach performs well on a self-annotated event-based pedestrian dataset with 8,736 event frames. This work paves the way of more fascinating perception applications with neuromorphic vision sensors.