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Found 11 papers, 7 papers with code
Contrastive Learning for Self-Supervised Pre-Training of Point Cloud Segmentation Networks With Image Data
Reducing the quantity of annotations required for supervised training is vital when labels are scarce and costly.
Self-Supervised Pre-training of 3D Point Cloud Networks with Image Data
Reducing the quantity of annotations required for supervised training is vital when labels are scarce and costly.
On the Coupling of Depth and Egomotion Networks for Self-Supervised Structure from Motion
Structure from motion (SfM) has recently been formulated as a self-supervised learning problem, where neural network models of depth and egomotion are learned jointly through view synthesis.
Learned Camera Gain and Exposure Control for Improved Visual Feature Detection and Matching
Successful visual navigation depends upon capturing images that contain sufficient useful information.
Self-Supervised Scale Recovery for Monocular Depth and Egomotion Estimation
The self-supervised loss formulation for jointly training depth and egomotion neural networks with monocular images is well studied and has demonstrated state-of-the-art accuracy.
Heteroscedastic Uncertainty for Robust Generative Latent Dynamics
Learning or identifying dynamics from a sequence of high-dimensional observations is a difficult challenge in many domains, including reinforcement learning and control.
Self-Supervised Deep Pose Corrections for Robust Visual Odometry
We present a self-supervised deep pose correction (DPC) network that applies pose corrections to a visual odometry estimator to improve its accuracy.
Robotics
Robust Data-Driven Zero-Velocity Detection for Foot-Mounted Inertial Navigation
We present two novel techniques for detecting zero-velocity events to improve foot-mounted inertial navigation.
Robotics Signal Processing
Probabilistic Regression of Rotations using Quaternion Averaging and a Deep Multi-Headed Network
Accurate estimates of rotation are crucial to vision-based motion estimation in augmented reality and robotics.
LSTM-Based Zero-Velocity Detection for Robust Inertial Navigation
While existing threshold-based zero-velocity detectors are not robust to varying motion types, our learned model accurately detects stationary periods of the inertial measurement unit (IMU) despite changes in the motion of the user.
Robotics Human-Computer Interaction
Improving Foot-Mounted Inertial Navigation Through Real-Time Motion Classification
We present a method to improve the accuracy of a foot-mounted, zero-velocity-aided inertial navigation system (INS) by varying estimator parameters based on a real-time classification of motion type.
Robotics Human-Computer Interaction
