Jing Liang
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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

Place Recognition

Oct 1010, 12120 · 1 min read
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Place recognition localize the robot in a global map given current observations. This task includes place recognition in indoor and outdoor scenarios with various sensors.

Last updated on Oct 1010, 12120
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Jing Liang
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Jing Liang
PhD Student
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Scene Understanding Oct 1010, 12120 →

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