Search Results for author:
Found 25 papers, 22 papers with code
BDIS-SLAM: A lightweight CPU-based dense stereo SLAM for surgery
Conclusion: The proposed BDIS-SLAM is a lightweight stereo dense SLAM system for MIS.
ConvBKI: Real-Time Probabilistic Semantic Mapping Network with Quantifiable Uncertainty
In this paper, we develop a modular neural network for real-time semantic mapping in uncertain environments, which explicitly updates per-voxel probabilistic distributions within a neural network layer.
Lie Neurons: Adjoint-Equivariant Neural Networks for Semisimple Lie Algebras
This paper proposes an equivariant neural network that takes data in any semi-simple Lie algebra as input.
4D Panoptic Segmentation as Invariant and Equivariant Field Prediction
In this paper, we develop rotation-equivariant neural networks for 4D panoptic segmentation.
Convolutional Bayesian Kernel Inference for 3D Semantic Mapping
Robotic perception is currently at a cross-roads between modern methods, which operate in an efficient latent space, and classical methods, which are mathematically founded and provide interpretable, trustworthy results.
SoLo T-DIRL: Socially-Aware Dynamic Local Planner based on Trajectory-Ranked Deep Inverse Reinforcement Learning
This work proposes a new framework for a socially-aware dynamic local planner in crowded environments by building on the recently proposed Trajectory-ranked Maximum Entropy Deep Inverse Reinforcement Learning (T-MEDIRL).
E2PN: Efficient SE(3)-Equivariant Point Network
We also propose a permutation layer to recover SO(3) features from spherical features to preserve the capacity to distinguish rotations.
BDIS: Bayesian Dense Inverse Searching Method for Real-Time Stereo Surgical Image Matching
The patch-based fast disparity searching algorithm is adopted for the rectified stereo images.
MotionSC: Data Set and Network for Real-Time Semantic Mapping in Dynamic Environments
This work addresses a gap in semantic scene completion (SSC) data by creating a novel outdoor data set with accurate and complete dynamic scenes.
Fusing Convolutional Neural Network and Geometric Constraint for Image-based Indoor Localization
This paper proposes a new image-based localization framework that explicitly localizes the camera/robot by fusing Convolutional Neural Network (CNN) and sequential images' geometric constraints.
Dynamic Semantic Occupancy Mapping using 3D Scene Flow and Closed-Form Bayesian Inference
This paper reports on a dynamic semantic mapping framework that incorporates 3D scene flow measurements into a closed-form Bayesian inference model.
Correspondence-Free Point Cloud Registration with SO(3)-Equivariant Implicit Shape Representations
We learn an embedding for each point cloud in a feature space that preserves the SO(3)-equivariance property, enabled by recent developments in equivariant neural networks.
Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery
Our Bayesian method correctly balances the probability of the patch for stereo images at different scales.
Global Unifying Intrinsic Calibration for Spinning and Solid-State LiDARs
By modeling the calibration parameters as an element of a special matrix Lie Group, we achieve a unifying view of calibration for different types of LiDARs.
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.
A Closed-Form Uncertainty Propagation in Non-Rigid Structure from Motion
In this paper, we present a statistical inference on the element-wise uncertainty quantification of the estimated deforming 3D shape points in the case of the exact low-rank SDP problem.
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.
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
Nonparametric Continuous Sensor Registration
The functions can be defined on arbitrary smooth manifolds where the action of a Lie group aligns them.
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.
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.
Bayesian Spatial Kernel Smoothing for ScalableDense Semantic Mapping
This paper develops a Bayesian continuous 3D semantic occupancy map from noisy point cloud measurements.
Robotics
LiDARTag: A Real-Time Fiducial Tag System for Point Clouds
Because of the LiDAR sensors' nature, rapidly changing ambient lighting will not affect the detection of a LiDARTag; hence, the proposed fiducial marker can operate in a completely dark environment.
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
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.
