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Found 13 papers, 9 papers with code
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.
A Smooth Representation of Belief over SO(3) for Deep Rotation Learning with Uncertainty
Accurate rotation estimation is at the heart of robot perception tasks such as visual odometry and object pose estimation.
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
Sparse Bounded Degree Sum of Squares Optimization for Certifiably Globally Optimal Rotation Averaging
Estimating unknown rotations from noisy measurements is an important step in SfM and other 3D vision tasks.
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.
Certifiably Globally Optimal Extrinsic Calibration from Per-Sensor Egomotion
We present a certifiably globally optimal algorithm for determining the extrinsic calibration between two sensors that are capable of producing independent egomotion estimates.
Robotics
DPC-Net: Deep Pose Correction for Visual Localization
We use this loss to train a Deep Pose Correction network (DPC-Net) that predicts corrections for a particular estimator, sensor and environment.
PROBE: Predictive Robust Estimation for Visual-Inertial Navigation
Navigation in unknown, chaotic environments continues to present a significant challenge for the robotics community.
PROBE-GK: Predictive Robust Estimation using Generalized Kernels
Many algorithms in computer vision and robotics make strong assumptions about uncertainty, and rely on the validity of these assumptions to produce accurate and consistent state estimates.
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
Reducing Drift in Visual Odometry by Inferring Sun Direction Using a Bayesian Convolutional Neural Network
We present a method to incorporate global orientation information from the sun into a visual odometry pipeline using only the existing image stream, where the sun is typically not visible.
Improving the Accuracy of Stereo Visual Odometry Using Visual Illumination Estimation
In the absence of reliable and accurate GPS, visual odometry (VO) has emerged as an effective means of estimating the egomotion of robotic vehicles.
