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A Consistency-Based Loss for Deep Odometry Through Uncertainty Propagation
In such cases where multiple losses are imposed on a network, the uncertainty over each output can be derived to weigh the different loss terms in a maximum likelihood setting.
Deep Inertial Odometry with Accurate IMU Preintegration
Inertial Measurement Units (IMUs) are interceptive modalities that provide ego-motion measurements independent of the environmental factors.
Exploring Self-Attention for Visual Odometry
Visual odometry networks commonly use pretrained optical flow networks in order to derive the ego-motion between consecutive frames.
ARC-Net: Activity Recognition Through Capsules
We provide heatmaps of the priors, learned by the network, to visualize the utilization of each of the data sources by the trained network.
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