Search Results for author:
Found 19 papers, 9 papers with code
MLOD: Awareness of Extrinsic Perturbation in Multi-LiDAR 3D Object Detection for Autonomous Driving
To minimize the detrimental effect of extrinsic perturbation, we propagate an uncertainty prior on each point of input point clouds, and use this information to boost an approach for 3D geometric tasks.
MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships
Monocular 3D object detection is an essential component in autonomous driving while challenging to solve, especially for those occluded samples which are only partially visible.
Ranked #11 on
Vehicle Pose Estimation
on KITTI Cars Hard
High-speed Autonomous Drifting with Deep Reinforcement Learning
Drifting is a complicated task for autonomous vehicle control.
Utilizing Eye Gaze to Enhance the Generalization of Imitation Networks to Unseen Environments
Vision-based autonomous driving through imitation learning mimics the behaviors of human drivers by training on pairs of data of raw driver-view images and actions.
Gaze Training by Modulated Dropout Improves Imitation Learning
The basic idea behind behavioral cloning is to have the neural network learn from observing a human expert's behavior.
Fully Using Classifiers for Weakly Supervised Semantic Segmentation with Modified Cues
This paper proposes a novel weakly-supervised semantic segmentation method using image-level label only.
Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware Perspective
End-to-end visual-based imitation learning has been widely applied in autonomous driving.
Focal Loss in 3D Object Detection
Inspired by the recent use of focal loss in image-based object detection, we extend this hard-mining improvement of binary cross entropy to point-cloud-based object detection and conduct experiments to show its performance based on two different 3D detectors: 3D-FCN and VoxelNet.
PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud
We take the spherical image, which is transformed from the 3D LiDAR point clouds, as input of the convolutional neural networks (CNNs) to predict the point-wise semantic map.
Curiosity-driven Exploration for Mapless Navigation with Deep Reinforcement Learning
A video of our experimental results can be found at https://goo. gl/pWbpcF.
VR-Goggles for Robots: Real-to-sim Domain Adaptation for Visual Control
In this paper, we deal with the reality gap from a novel perspective, targeting transferring Deep Reinforcement Learning (DRL) policies learned in simulated environments to the real-world domain for visual control tasks.
Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning
Experiments show that our GAIL-based approach greatly improves the safety and efficiency of the behavior of mobile robots from pure behavior cloning.
Towards continuous control of flippers for a multi-terrain robot using deep reinforcement learning
In this paper we focus on developing a control algorithm for multi-terrain tracked robots with flippers using a reinforcement learning (RL) approach.
Neural SLAM: Learning to Explore with External Memory
We present an approach for agents to learn representations of a global map from sensor data, to aid their exploration in new environments.
Reinforcement Learning (RL)
Simultaneous Localization and Mapping
Extrinsic Calibration of 3D Range Finder and Camera without Auxiliary Object or Human Intervention
In this paper, we proposed a novel extrinsic calibration approach for the extrinsic calibration of range and image sensors.
Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation
We present a learning-based mapless motion planner by taking the sparse 10-dimensional range findings and the target position with respect to the mobile robot coordinate frame as input and the continuous steering commands as output.
A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation
We carry out our discussions on the two main paradigms for learning control with deep networks: deep reinforcement learning and imitation learning.
Towards Cognitive Exploration through Deep Reinforcement Learning for Mobile Robots
We believe it is the first time that raw sensor information is used to build cognitive exploration strategy for mobile robots through end-to-end deep reinforcement learning.
PCA-aided Fully Convolutional Networks for Semantic Segmentation of Multi-channel fMRI
The results of the convolutional network are compared with various methods e. g. k-NN.
