4 citations · 13 across the 19 of their papers we have counts for
19 papers
ForestSim: A Synthetic Benchmark for Intelligent Vehicle Perception in Unstructured Forest Environments
Pragat Wagle, Zheng Chen, Lantao Liu
Robust scene understanding is essential for intelligent vehicles operating in natural, unstructured environments. While semantic segmentation datasets for structured urban driving…
Navigating the Wild: Pareto-Optimal Visual Decision-Making in Image Space
Durgakant Pushp, Weizhe Chen, Zheng Chen +3
Navigating complex real-world environments requires semantic understanding and adaptive decision-making. Traditional reactive methods without maps often fail in cluttered settings,…
C^2DA: Contrastive and Context-aware Domain Adaptive Semantic Segmentation
Md. Al-Masrur Khan, Zheng Chen, Lantao Liu
Unsupervised domain adaptive semantic segmentation (UDA-SS) aims to train a model on the source domain data (e.g., synthetic) and adapt the model to predict target domain data (e.g…
Context-Generative Default Policy for Bounded Rational Agent
Durgakant Pushp, Junhong Xu, Zheng Chen +1
Bounded rational agents often make decisions by evaluating a finite selection of choices, typically derived from a reference point termed the default policy,' based on previous…
Visual-Geometry GP-based Navigable Space for Autonomous Navigation
Mahmoud Ali, Durgkant Pushp, Zheng Chen +1
Autonomous navigation in unknown environments is challenging and demands the consideration of both geometric and semantic information in order to parse the navigability of the envi…
POVNav: A Pareto-Optimal Mapless Visual Navigator
Durgakant Pushp, Zheng Chen, Chaomin Luo +2
Mapless navigation has emerged as a promising approach for enabling autonomous robots to navigate in environments where pre-existing maps may be inaccurate, outdated, or unavailabl…
Pseudo-Trilateral Adversarial Training for Domain Adaptive Traversability Prediction
Zheng Chen, Durgakant Pushp, Jason M. Gregory +1
Traversability prediction is a fundamental perception capability for autonomous navigation. Deep neural networks (DNNs) have been widely used to predict traversability during the l…
IDA: Informed Domain Adaptive Semantic Segmentation
Zheng Chen, Zhengming Ding, Jason M. Gregory +1
Mixup-based data augmentation has been validated to be a critical stage in the self-training framework for unsupervised domain adaptive semantic segmentation (UDA-SS), which aims t…
SePaint: Semantic Map Inpainting via Multinomial Diffusion
Zheng Chen, Deepak Duggirala, David Crandall +2
Prediction beyond partial observations is crucial for robots to navigate in unknown environments because it can provide extra information regarding the surroundings beyond the curr…
CALI: Coarse-to-Fine ALIgnments Based Unsupervised Domain Adaptation of Traversability Prediction for Deployable Autonomous Navigation
Zheng Chen, Durgakant Pushp, Lantao Liu
Traversability prediction is a fundamental perception capability for autonomous navigation. The diversity of data in different domains imposes significant gaps to the prediction pe…
Kernel-based diffusion approximated Markov decision processes for autonomous navigation and control on unstructured terrains
Junhong Xu, Kai Yin, Zheng Chen +3
We propose a diffusion approximation method to the continuous-state Markov Decision Processes (MDPs) that can be utilized to address autonomous navigation and control in unstructur…
Polyline Generative Navigable Space Segmentation for Autonomous Visual Navigation
Zheng Chen, Zhengming Ding, David Crandall +1
Detecting navigable space is a fundamental capability for mobile robots navigating in unknown or unmapped environments. In this work, we treat visual navigable space segmentation a…
NSS-VAEs: Generative Scene Decomposition for Visual Navigable Space Construction
Zheng Chen, Lantao Liu
Detecting navigable space is the first and also a critical step for successful robot navigation. In this work, we treat the visual navigable space segmentation as a scene decomposi…
Efficient Map Prediction via Low-Rank Matrix Completion
Zheng Chen, Shi Bai, Lantao Liu
In many autonomous mapping tasks, the maps cannot be accurately constructed due to various reasons such as sparse, noisy, and partial sensor measurements. We propose a novel map pr…
Multi-Objective Autonomous Exploration on Real-Time Continuous Occupancy Maps
Zheng Chen, Weizhe Chen, Shi Bai +1
Autonomous exploration in unknown environments using mobile robots is the pillar of many robotic applications. Existing exploration frameworks either select the nearest geometric f…
Solving the linear transport equation by a deep neural network approach
Zheng Chen, Liu Liu, Lin Mu
In this paper, we study the linear transport model by adopting the deep learning method, in particular the deep neural network (DNN) approach. While the interest of using DNN to st…
Optimizing Information Freshness in a Multiple Access Channel with Heterogeneous Devices
Zheng Chen, Nikolaos Pappas, Emil Björnson +1
In this work, we study age-optimal scheduling with stability constraints in a multiple access channel with two heterogeneous source nodes transmitting to a common destination. The…
Creating Navigable Space from Sparse Noisy Map Points
Zheng Chen, Lantao Liu
We present a framework for creating navigable space from sparse and noisy map points generated by sparse visual SLAM methods. Our method incrementally seeds and creates local conve…
On the Performance of Delay Aware Shared Access with Priorities
Zheng Chen, Nikolaos Pappas, Marios Kountouris +1
In this paper, we analyze a shared access network with a fixed primary node and randomly distributed secondary nodes whose distribution follows a Poisson point process (PPP). The s…