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Found 10 papers, 1 papers with code
Object Memory Transformer for Object Goal Navigation
This paper presents a reinforcement learning method for object goal navigation (ObjNav) where an agent navigates in 3D indoor environments to reach a target object based on long-term observations of objects and scenes.
Adaptive Future Frame Prediction with Ensemble Network
A common limitation of the existing learning-based approaches is a mismatch of training data and test data.
Deep Reactive Planning in Dynamic Environments
The main novelty of the proposed approach is that it allows a robot to learn an end-to-end policy which can adapt to changes in the environment during execution.
Self-supervised Neural Audio-Visual Sound Source Localization via Probabilistic Spatial Modeling
By incorporating with the spatial information in multichannel audio signals, our method trains deep neural networks (DNNs) to distinguish multiple sound source objects.
YOLO and K-Means Based 3D Object Detection Method on Image and Point Cloud
In our research, Camera can capture the image to make the Real-time 2D Object Detection by using YOLO, I transfer the bounding box to node whose function is making 3d object detection on point cloud data from Lidar.
3D Object Detection Method Based on YOLO and K-Means for Image and Point Clouds
In our research, camera can capture the image to make the Real-time 2D object detection by using YOLO, we transfer the bounding box to node whose function is making 3d object detection on point cloud data from Lidar.
Learning-Based Human Segmentation and Velocity Estimation Using Automatic Labeled LiDAR Sequence for Training
In this paper, we propose an automatic labeled sequential data generation pipeline for human segmentation and velocity estimation with point clouds.
Efficient Exploration in Constrained Environments with Goal-Oriented Reference Path
Specifically, we train a deep convolutional network that can predict collision-free paths based on a map of the environment-- this is then used by a reinforcement learning algorithm to learn to closely follow the path.
Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model
This paper presents an unsupervised method that trains neural source separation by using only multichannel mixture signals.
Automatic Labeled LiDAR Data Generation based on Precise Human Model
We present 500k+ data generated by the proposed pipeline.
