Panoramic Multimodal Semantic Occupancy Prediction for Quadruped Robots

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Talent Scout

G

Guoqiang Zhao

Hunan University

Z

Zhe Yang

Hunan University

S

Sheng Wu

Hunan University

F

Fei Teng

Hunan University

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References (76)

[1]
OneOcc: Semantic Occupancy Prediction for Legged Robots with a Single Panoramic Camera
2025Hao Shi, Ze Wang et al.
[2]
Advancing Off-Road Autonomous Driving: The Large-Scale ORAD-3D Dataset and Comprehensive Benchmarks
2025Chen Min, Jilin Mei et al.
[3]
FishBEV: Distortion-Resilient Bird's Eye View Segmentation with Surround-View Fisheye Cameras
2025Hang Li, Dianmo Sheng et al.
[4]
Deformable Spherical Geometry Transformer For Panoramic Semantic Segmentation
2025Boyang Lan, Li Yang et al.
[5]
One Flight Over the Gap: A Survey from Perspective to Panoramic Vision
2025Xin Lin, Xian Ge et al.
[6]
QuaDreamer: Controllable Panoramic Video Generation for Quadruped Robots
2025Sheng Wu, Fei Teng et al.
[7]
ArticuBEVSeg: Road Semantic Understanding and its Application in Bird's Eye View From Panoramic Vision System of Long Combination Vehicles
2025Weimin Liu, Wenjun Wang
[8]
SDGOCC: Semantic and Depth-Guided Bird’s-Eye View Transformation for 3D Multimodal Occupancy Prediction
2025Zaipeng Duan, Chenxu Dang et al.
[9]
VoxDet: Rethinking 3D Semantic Occupancy Prediction as Dense Object Detection
2025Wuyang Li, Zhu Yu et al.
[10]
EmbodiedOcc++: Boosting Embodied 3D Occupancy Prediction with Plane Regularization and Uncertainty Sampler
2025Hao Wang, Xiaobao Wei et al.
[11]
Mamba4PASS: Vision Mamba for PAnoramic Semantic Segmentation
2025Jiayue Xu, Chao Xu et al.
[12]
3D Occupancy Prediction with Low-Resolution Queries via Prototype-aware View Transformation
2025Gyeongrok Oh, Sungjune Kim et al.
[13]
HumanoidPano: Hybrid Spherical Panoramic-LiDAR Cross-Modal Perception for Humanoid Robots
2025Qiang Zhang, Zhang Zhang et al.
[14]
OmniSAM: Omnidirectional Segment Anything Model for UDA in Panoramic Semantic Segmentation
2025Ding Zhong, Xu Zheng et al.
[15]
Omnidirectional Multi-Object Tracking
2025Kai Luo, Haowen Shi et al.
[16]
Dur360BEV: A Real-World 360-Degree Single Camera Dataset and Benchmark for Bird-Eye View Mapping in Autonomous Driving
2025E. Wenke, Chao Yuan et al.
[17]
DASC-SPT: Towards Self-Supervised Panoramic Semantic Segmentation
2025Tianlong Tan, Bin Chen et al.
[18]
A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision
2025Hao Ai, Zidong Cao et al.
[19]
A Synthetic Benchmark for Collaborative 3D Semantic Occupancy Prediction in V2X Autonomous Driving
2025Hanlin Wu, Pengfei Lin et al.
[20]
OmniHD-Scenes: A Next-Generation Multimodal Dataset for Autonomous Driving
2024Lianqing Zheng, Long Yang et al.

Showing 20 of 76 references

Founder's Pitch

"Develop VoxelHound, a panoramic multimodal perception framework for quadruped robots, using the new PanoMMOcc dataset."

RoboticsScore: 8View PDF ↗

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0-10 scale

High Potential

3/4 signals

7.5

Quick Build

2/4 signals

5

Series A Potential

4/4 signals

10

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Why It Matters

This research matters because it addresses the unique challenges faced by quadruped robots in navigating complex environments using panoramic images for full environmental coverage, which is not adequately supported by existing datasets and methods primarily focused on wheeled robots.

Product Angle

To productize this, the system could be developed into a software SDK that can be integrated with existing quadruped robots, providing them with enhanced environmental perception capabilities out of the box.

Disruption

This solution could replace traditional navigation solutions in quadrupeds that rely mainly on single modal sensing or wheeled robot technologies that are inadequate for dynamic, unstructured environments.

Product Opportunity

The market size for service and exploratory robots is growing, with companies and institutions likely to pay for advanced navigation capabilities in robots operating in complex, unstructured environments.

Use Case Idea

A commercial application could involve equipping delivery robots with this system to navigate dynamic indoor and outdoor environments autonomously, leveraging the multimodal data fusion for precise obstacle avoidance and path planning.

Science

The approach involves creating a panoramic multimodal semantic occupancy prediction framework named VoxelHound. It specifically addresses vertical jitter caused by quadruped mobility, using a module for compensation, and fuses multimodal signals like RGB, thermal, polarization, and LiDAR into a unified representation for better environmental understanding.

Method & Eval

The system was tested using the new PanoMMOcc dataset and achieved state-of-the-art performance with a 4.16% improvement in mIoU over existing methods, showing robustness across various dynamic scenes and sensor modalities.

Caveats

The approach requires integration with a range of sensors, potentially increasing the hardware cost and complexity for deployment. Low-light or extreme weather conditions might still pose challenges despite multimodal data integration.

Author Intelligence

Guoqiang Zhao

Hunan University

Zhe Yang

Hunan University

Sheng Wu

Hunan University

Fei Teng

Hunan University

Mengfei Duan

Hunan University

Yuanfan Zheng

Hunan University

Kai Luo

Hunan University

Kailun Yang

Hunan University
kailun.yang@hnu.edu.cn

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