Jing Liang
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  • Papers
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  • Projects
    • Datasets
    • Indoor Navigation
    • Language-Model-Based Approaches
    • Outdoor Navigation
    • Place Recognition
    • Scene Understanding
  • Language-Model-Based Approaches
    • BehAV: Behavioral Rule Guided Autonomy Using VLMs for Robot Navigation in Outdoor Scenes
    • On the safety concerns of deploying llms/vlms in robotics: Highlighting the risks and vulnerabilities
    • TGS: Trajectory Generation and Selection using Vision Language Models in Mapless Outdoor Environments
    • VLM-Social-Nav: Socially Aware Robot Navigation through Scoring using Vision-Language Models
  • Place Recognition
    • CSCPR: Cross-Source-Context Indoor RGB-D Place Recognition
    • PoCo: Point Context Cluster for RGBD Indoor Place Recognition
    • Visual, Spatial, Geometric-Preserved Place Recognition for Cross-View and Cross-Modal Collaborative Perception
    • Terrapn: Unstructured terrain navigation using online self-supervised learning
  • Scene Understanding
    • ET-Former: Efficient Triplane Deformable Attention for 3D Semantic Scene Completion From Monocular Camera
  • Datasets
    • GND: Global Navigation Dataset with Multi-Modal Perception and Multi-Category Traversability in Outdoor Campus Environments
  • Outdoor Navigation
    • Deep Stochastic Kinematic Models for Probabilistic Motion Forecasting in Traffic
    • DTG: Diffusion-based Trajectory Generation for Mapless Global Navigation
    • Mtg: Mapless trajectory generator with traversability coverage for outdoor navigation
    • GrASPE: Graph based Multimodal Fusion for Robot Navigation in Outdoor Environments
    • Adaptiveon: Adaptive outdoor local navigation method for stable and reliable actions
    • Terrapn: Unstructured terrain navigation using online self-supervised learning
    • Autonomous social distancing in urban environments using a quadruped robot
  • Projects
  • Experience
  • Indoor Navigation
    • MSVIPER
    • Image-goal navigation in complex environments via modular learning
    • XAI-N: Sensor-based robot navigation using expert policies and decision trees
    • OF-VO: Efficient Navigation Among Pedestrians Using Commodity Sensors
    • Crowd-steer: Realtime smooth and collision-free robot navigation in densely crowded scenarios trained using high-fidelity simulation
    • DenseCAvoid: Real-time Navigation in Dense Crowds using Anticipatory Behaviors

Indoor Navigation

Oct 1010, 12120 · 1 min read
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Indoor Navigation solves the problem of moving robots in indoor scenarios. It includes indoor collision avoidance and indoor social navigation.

Last updated on Oct 1010, 12120
Projects
Jing Liang
Authors
Jing Liang
PhD Student
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