  Object segmentation for robotic grasping under dynamic conditions often faces
challenges such as occlusion, low light conditions, motion blur and object size
variance. To address these challenges, we propose a Deep Learning network that
fuses two types of visual signals, event-based data and RGB frame data. The
proposed Bimodal SegNet network has two distinct encoders, one for each signal
input and a spatial pyramidal pooling with atrous convolutions. Encoders
capture rich contextual information by pooling the concatenated features at
different resolutions while the decoder obtains sharp object boundaries. The
evaluation of the proposed method undertakes five unique image degradation
challenges including occlusion, blur, brightness, trajectory and scale variance
on the Event-based Segmentation (ESD) Dataset. The evaluation results show a
6-10\% segmentation accuracy improvement over state-of-the-art methods in terms
of mean intersection over the union and pixel accuracy. The model code is
available at https://github.com/sanket0707/Bimodal-SegNet.git
