Search Results for author:
Found 17 papers, 9 papers with code
Hard Cases Detection in Motion Prediction by Vision-Language Foundation Models
However, the rarity and high-risk nature of these cases demand extensive, diverse datasets for training robust models.
Benchmarking Classical and Learning-Based Multibeam Point Cloud Registration
However, in the underwater domain, most registration of multibeam echo-sounder (MBES) point cloud data are still performed using classical methods in the iterative closest point (ICP) family.
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.
Deep Reinforcement Learning to Acquire Navigation Skills for Wheel-Legged Robots in Complex Environments
Mobile robot navigation in complex and dynamic environments is a challenging but important problem.
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.
