13 citations · 17 across the 6 of their papers we have counts for
6 papers
Breaking the Static Assumption: A Dynamic-Aware LIO Framework Via Spatio-Temporal Normal Analysis
Chen Zhiqiang, Le Gentil Cedric, Lin Fuling +5
This paper addresses the challenge of Lidar-Inertial Odometry (LIO) in dynamic environments, where conventional methods often fail due to their static-world assumptions. Traditiona…
Flying in Highly Dynamic Environments with End-to-end Learning Approach
Xiyu Fan, Minghao Lu, Bowen Xu +1
Obstacle avoidance for unmanned aerial vehicles like quadrotors is a popular research topic. Most existing research focuses only on static environments, and obstacle avoidance in e…
Heterogeneous LiDAR Dataset for Benchmarking Robust Localization in Diverse Degenerate Scenarios
Zhiqiang Chen, Yuhua Qi, Dapeng Feng +6
The ability to estimate pose and generate maps using 3D LiDAR significantly enhances robotic system autonomy. However, existing open-source datasets lack representation of geometri…
FAPP: Fast and Adaptive Perception and Planning for UAVs in Dynamic Cluttered Environments
Minghao Lu, Xiyu Fan, Han Chen +1
Obstacle avoidance for Unmanned Aerial Vehicles (UAVs) in cluttered environments is significantly challenging. Existing obstacle avoidance for UAVs either focuses on fully static e…
Learning Agile Flights through Narrow Gaps with Varying Angles using Onboard Sensing
Yuhan Xie, Minghao Lu, Rui Peng +1
This paper addresses the problem of traversing through unknown, tilted, and narrow gaps for quadrotors using Deep Reinforcement Learning (DRL). Previous learning-based methods reli…
Flying Through a Narrow Gap Using End-to-end Deep Reinforcement Learning Augmented with Curriculum Learning and Sim2Real
Chenxi Xiao, Peng Lu, Qizhi He
Traversing through a tilted narrow gap is previously an intractable task for reinforcement learning mainly due to two challenges. First, searching feasible trajectories is not triv…