Search Results for author:
Found 12 papers, 10 papers with code
WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images
Due to the depth-dependent water column effects inherent to underwater environments, we show that our end-to-end network implicitly learns a coarse depth estimate of the underwater scene from monocular underwater images.
Legged Robot State-Estimation Through Combined Forward Kinematic and Preintegrated Contact Factors
We introduce forward kinematic factors and preintegrated contact factors into a factor graph framework that can be incrementally solved in real-time.
Robotics
Hybrid Contact Preintegration for Visual-Inertial-Contact State Estimation Using Factor Graphs
The factor graph framework is a convenient modeling technique for robotic state estimation where states are represented as nodes, and measurements are modeled as factors.
Robotics
Contact-Aided Invariant Extended Kalman Filtering for Legged Robot State Estimation
On the basis of the theory of invariant observer design by Barrau and Bonnabel, and in particular, the Invariant EKF (InEKF), we show that the error dynamics of the point contact-inertial system follows a log-linear autonomous differential equation; hence, the observable state variables can be rendered convergent with a domain of attraction that is independent of the system's trajectory.
Robotics
Continuous Direct Sparse Visual Odometry from RGB-D Images
This paper reports on a novel formulation and evaluation of visual odometry from RGB-D images.
Contact-Aided Invariant Extended Kalman Filtering for Robot State Estimation
This filter combines contact-inertial dynamics with forward kinematic corrections to estimate pose and velocity along with all current contact points.
Robotics
Bayesian Spatial Kernel Smoothing for ScalableDense Semantic Mapping
This paper develops a Bayesian continuous 3D semantic occupancy map from noisy point cloud measurements.
Robotics
Adaptive Continuous Visual Odometry from RGB-D Images
In this paper, we extend the recently developed continuous visual odometry framework for RGB-D cameras to an adaptive framework via online hyperparameter learning.
A Keyframe-based Continuous Visual SLAM for RGB-D Cameras via Nonparametric Joint Geometric and Appearance Representation
The experimental evaluations using publicly available RGB-D benchmarks show that the developed keyframe selection technique using continuous visual odometry outperforms its robust dense (and direct) visual odometry equivalent.
DeepLocNet: Deep Observation Classification and Ranging Bias Regression for Radio Positioning Systems
The proposed algorithm can globally localize and track a smartphone (or robot) with a priori unknown location, and with a semi-accurate prior map (error within 0. 8 m) of the WiFi Access Points (AP).
Robotics Signal Processing
Monocular Depth Prediction through Continuous 3D Loss
In contrast to the current point-to-point loss evaluation approach, the proposed 3D loss treats point clouds as continuous objects; therefore, it compensates for the lack of dense ground truth depth due to LIDAR's sparsity measurements.
A New Framework for Registration of Semantic Point Clouds from Stereo and RGB-D Cameras
This paper reports on a novel nonparametric rigid point cloud registration framework that jointly integrates geometric and semantic measurements such as color or semantic labels into the alignment process and does not require explicit data association.
