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Found 25 papers, 5 papers with code
Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm
The model was trained to predict the regional genetic alteration of three GBM driver genes (EGFR, PDGFRA, and PTEN) based on features extracted from the corresponding region of five MRI contrast images.
A Novel Hybrid Ordinal Learning Model with Health Care Application
This situation is quite common in health care datasets due to limitations of the diagnostic instrument, sparse clinical visits, or/and patient dropout.
A Dynamic Points Removal Benchmark in Point Cloud Maps
In the field of robotics, the point cloud has become an essential map representation.
FSNet: Redesign Self-Supervised MonoDepth for Full-Scale Depth Prediction for Autonomous Driving
Predicting accurate depth with monocular images is important for low-cost robotic applications and autonomous driving.
An Efficient Approach to the Online Multi-Agent Path Finding Problem by Using Sustainable Information
The low-level solver, the Sustainable Reverse Safe Interval Path Planning algorithm (SRSIPP), is an efficient single-agent solver that uses previous planning context to reduce duplicate calculations.
RNGDet++: Road Network Graph Detection by Transformer with Instance Segmentation and Multi-scale Features Enhancement
To annotate road network graphs effectively and efficiently, automatic algorithms for road network graph detection are demanded.
CenterLineDet: CenterLine Graph Detection for Road Lanes with Vehicle-mounted Sensors by Transformer for HD Map Generation
Due to the use of the DETR-like transformer network, CenterLineDet can handle complicated graph topology, such as lane intersections.
MMFN: Multi-Modal-Fusion-Net for End-to-End Driving
Inspired by the fact that humans use diverse sensory organs to perceive the world, sensors with different modalities are deployed in end-to-end driving to obtain the global context of the 3D scene.
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RNGDet: Road Network Graph Detection by Transformer in Aerial Images
To provide a solution to these problems, we propose a novel approach based on transformer and imitation learning in this paper.
csBoundary: City-scale Road-boundary Detection in Aerial Images for High-definition Maps
To provide a solution to the aforementioned problems, in this letter, we propose a novel system termed csBoundary to automatically detect road boundaries at the city scale for HD map annotation.
CP-loss: Connectivity-preserving Loss for Road Curb Detection in Autonomous Driving with Aerial Images
To alleviate this issue, we detect road curbs offline using high-resolution aerial images in this paper.
Learning Interpretable End-to-End Vision-Based Motion Planning for Autonomous Driving with Optical Flow Distillation
To address this problem, we propose an interpretable end-to-end vision-based motion planning approach for autonomous driving, referred to as IVMP.
Greedy-Based Feature Selection for Efficient LiDAR SLAM
Modern LiDAR-SLAM (L-SLAM) systems have shown excellent results in large-scale, real-world scenarios.
YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection
Object detection in 3D with stereo cameras is an important problem in computer vision, and is particularly crucial in low-cost autonomous mobile robots without LiDARs.
3D Object Detection From Stereo Images
Disparity Estimation
+3
Peer-Assisted Robotic Learning: A Data-Driven Collaborative Learning Approach for Cloud Robotic Systems
Both data and models are shared by robots to the cloud after semantic computing and training locally.
The Role of the Hercules Autonomous Vehicle During the COVID-19 Pandemic: An Autonomous Logistic Vehicle for Contactless Goods Transportation
Moreover, many countries have imposed tough lockdown measures to reduce the virus transmission (e. g., retail, catering) during the pandemic, which causes inconveniences for human daily life.
Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Data
Compared with transfer learning and meta-learning, FIL is more suitable to be deployed in cloud robotic systems.
A Robust Stereo Camera Localization Method with Prior LiDAR Map Constrains
Different from the conventional visual localization system, we design a novel visual optimization model by matching planar information between the LiDAR map and visual image.
Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Data
The experimental results demonstrate that FIL is capable of increasing imitation learning of local robots in cloud robotic systems.
A Robust Roll Angle Estimation Algorithm Based on Gradient Descent
This paper presents a robust roll angle estimation algorithm, which is developed from our previously published work, where the roll angle was estimated from a dense disparity map by minimizing a global energy using golden section search algorithm.
A Novel Dual-Lidar Calibration Algorithm Using Planar Surfaces
Multiple lidars are prevalently used on mobile vehicles for rendering a broad view to enhance the performance of localization and perception systems.
Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
To address the problem, we present a learning architecture for navigation in cloud robotic systems: Lifelong Federated Reinforcement Learning (LFRL).
Multiple Lane Detection Algorithm Based on Optimised Dense Disparity Map Estimation
Lane detection is very important for self-driving vehicles.
Real-Time Subpixel Fast Bilateral Stereo
Stereo vision technique has been widely used in robotic systems to acquire 3-D information.
