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Object segmentation for robotic grasping under dynamic conditions often faceschallenges such as occlusion, low light conditions, motion blur and object sizevariance. To address these challenges, we propose a Deep Learning network thatfuses two types of visual signals, event-based data and RGB frame data. Theproposed Bimodal SegNet network has two distinct encoders, one for each signalinput and a spatial pyramidal pooling with atrous convolutions. Encoderscapture rich contextual information by pooling the concatenated features atdifferent resolutions while the decoder obtains sharp object boundaries. Theevaluation of the proposed method undertakes five unique image degradationchallenges including occlusion, blur, brightness, trajectory and scale varianceon the Event-based Segmentation (ESD) Dataset. The evaluation results show a6-10\% segmentation accuracy improvement over state-of-the-art methods in termsof mean intersection over the union and pixel accuracy. The model code isavailable at https://github.com/sanket0707/Bimodal-SegNet.git

Bimodal SegNet: Instance Segmentation Fusing Events and RGB Frames for Robotic Grasping