Autonomous driving paper index
MoNA Bench: A Benchmark for Monocular Depth Estimation in Navigation of Autonomous Unmanned Aircraft System
One-line summary
We present MoNA Bench, a benchmark for Monocular depth estimation in Navigation of the Autonomous unmanned Aircraft system (MoNA), emphasizing its obstacle avoidance and safe target tracking capabilities.
Engineering notes
We present MoNA Bench, a benchmark for Monocular depth estimation in Navigation of the Autonomous unmanned Aircraft system (MoNA), emphasizing its obstacle avoidance and safe target tracking capabilities.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Efficient trajectory and path planning (TPP) is essential for unmanned aircraft systems (UASs) autonomy in challenging environments. Despite the scale ambiguity inherent in monocular vision, characteristics like compact size make a monocular camera ideal for micro-aerial vehicle (MAV)-based UASs. This work introduces a real-time MAV system using monocular depth estimation (MDE) with novel scale recovery module for autonomous navigation. We present MoNA Bench, a benchmark for Monocular depth estimation in Navigation of the Autonomous unmanned Aircraft system (MoNA), emphasizing its obstacle avoidance and safe target tracking capabilities. We highlight key attributes—estimation efficiency, depth map accuracy, and scale consistency—for efficient TPP through MDE.
Links and sources
Need this topic turned into a technical roadmap?
Full Self Driving can prepare a custom autonomous driving literature review, code map, dataset map, and B2B technology assessment.
Request B2B research
Comments