9 accepted papers
Diffusion models have been promising for trajectory generation tasks due to their ability to capture multimodal behaviors, but their closed-box nature and unconstrained denoising often undermine kinodynamic feasibility. We propose a novel Coupled-Layer Diffusion Model (CLDM) that explicitly embeds a
Current AAV autonomous flights exhibit efficient performance in both indoor and field environments. However, they often face significant challenges in large-scale and cluttered environments, where the vast amount of captured data can lead to computation and storage bottlenecks. Additionally, the existin…
Current UAV autonomous flights exhibit efficient performance in both indoor and field environments. However, they often face significant challenges in large-scale and cluttered environments, where the vast amount of captured data can lead to computation and storage bottlenecks. Additionally, the exi
Indoor disaster relief and rescue missions require quadrotors to fully exploit their maneuverability in real-time. However, the computational complexity induced by the underactuated kinodynamics conflicts with the rapid replanning requirement. For agile trajectory planning in cluttered and unknown e
Building suitable representations for diversified environments to enable robot autonomous navigation is a complicated task, especially for large-scale environments, where the captured vast amount of data will give rise to computation and storage bottlenecks. In this letter, we first propose the rand
The vast amount of data captured by robots in large-scale environments brings the computing and storage bottlenecks to the typical methods of modeling the spaces the robots travel in. In order to efficiently construct a compact terrain model from uncertain, incomplete point cloud data of large-scale…
Generating meaningful spatial models of physical environments is a crucial ability for autonomous navigation of mobile robots. This paper considers the problem of building continuous occupancy maps from sparse and noisy sensor data. To this end, we propose a new method named random mapping maps that…
To address the problem of building accurate and coherent models for large-scale terrains from incomplete and noisy sensor data, this paper proposes a novel framework that can efficiently infer terrain structures by divisionally providing the best linear unbiased estimates for the elevation values. T…
Pedestrian trajectories and actions prediction in complex environment is challenging due to the complexity of human behavior and a variety of internal and external stimuli. Much works has gone towards predicting trajectories and actions separately without mining the coupling relationships between th…