Search Results for author:
Found 15 papers, 7 papers with code
Human-Centric Autonomous Systems With LLMs for User Command Reasoning
The evolution of autonomous driving has made remarkable advancements in recent years, evolving into a tangible reality.
RMP: A Random Mask Pretrain Framework for Motion Prediction
As the pretraining technique is growing in popularity, little work has been done on pretrained learning-based motion prediction methods in autonomous driving.
Evaluation of a Canonical Image Representation for Sidescan Sonar
In this paper, a canonical transformation method consisting of intensity correction and slant range correction is proposed to decrease the above distortion.
Online Stochastic Variational Gaussian Process Mapping for Large-Scale SLAM in Real Time
Autonomous underwater vehicles (AUVs) are becoming standard tools for underwater exploration and seabed mapping in both scientific and industrial applications \cite{graham2022rapid, stenius2022system}.
Neural Network Normal Estimation and Bathymetry Reconstruction from Sidescan Sonar
In this article, we use a neural network to represent the map and optimize it under constraints of altimeter points and estimated surface normal from sidescan.
High-Resolution Bathymetric Reconstruction From Sidescan Sonar With Deep Neural Networks
This is then combined with the indirect but high-resolution seabed slope information from the sidescan to estimate the full bathymetry.
Interpretability in Contact-Rich Manipulation via Kinodynamic Images
In this work, we address the interpretability of NN-based models by introducing the kinodynamic images.
Robotics
PointNetKL: Deep Inference for GICP Covariance Estimation in Bathymetric SLAM
However, an accurate estimate of the uncertainty of such registration is a key requirement to a consistent fusion of this kind of measurements in a SLAM filter.
Interpreting video features: a comparison of 3D convolutional networks and convolutional LSTM networks
A number of techniques for interpretability have been presented for deep learning in computer vision, typically with the goal of understanding what the networks have based their classification on.
Sparse2Dense: From direct sparse odometry to dense 3D reconstruction
In this paper, we proposed a new deep learning based dense monocular SLAM method.
GCNv2: Efficient Correspondence Prediction for Real-Time SLAM
In this paper, we present a deep learning-based network, GCNv2, for generation of keypoints and descriptors.
Detection and Tracking of General Movable Objects in Large 3D Maps
This paper studies the problem of detection and tracking of general objects with long-term dynamics, observed by a mobile robot moving in a large environment.
Unsupervised Object Discovery and Segmentation of RGBD-images
In this paper we introduce a system for unsupervised object discovery and segmentation of RGBD-images.
Adaptive Cost Function for Pointcloud Registration
In this paper we introduce an adaptive cost function for pointcloud registration.
