Papers with Code 아카이브

Drone navigation

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하위 태스크: 0
평가표: 1
보존된 평가 기록: 6

(Satellite -> Drone) Given one satellite-view image, the drone intends to find the most relevant place (drone-view images) that it has passed by. According to its flight history, the drone could be navigated back to the target place.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/IVIPLab/FBSNet

공식 코드

FBSNet: A Fast Bilateral Symmetrical Network for Real-Time Semantic Segmentation

https://github.com/MultimodalGeo/GeoText-1652

공식 코드

Towards Natural Language-Guided Drones: GeoText-1652 Benchmark with Spatial Relation Matching

https://github.com/dmmm1997/fsra

공식 코드

A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization

https://github.com/ercbunny/isaacgymenvs

공식 코드

Learning Generalizable Policy for Obstacle-Aware Autonomous Drone Racing

https://github.com/layumi/University1652-Baseline

공식 코드

University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization

https://github.com/lt-asset/selp

공식 코드

SELP: Generating Safe and Efficient Task Plans for Robot Agents with Large Language Models

https://github.com/microsoft/AirSim-Drone-Racing-VAE-Imitation

공식 코드

Learning Visuomotor Policies for Aerial Navigation Using Cross-Modal Representations

https://github.com/ntu-icg/fm-planner

공식 코드

FM-Planner: Foundation Model Guided Path Planning for Autonomous Drone Navigation

https://github.com/patchadversarialattacks/patchadversarialattacks

공식 코드

Physical Passive Patch Adversarial Attacks on Visual Odometry Systems

https://github.com/torrvision/straighttoshapes

공식 코드

Straight to Shapes++: Real-time Instance Segmentation Made More Accurate

https://github.com/tudelft/risk-sensitive-rl

공식 코드

Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement Learning

https://github.com/wtyhub/LPN

공식 코드

Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization

https://github.com/AggMan96/RK-Net

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.