12 accepted papers
Diffusion-based text-to-image (T2I) models have demonstrated remarkable advancements in generating high-quality images. However, while real-world applications like product packaging and logo design necessitate synthesis within irregular geometries, existing methods struggle to handle such constraint…
Despite significant advancements in the research of aquatic-aerial robots, existing configurations struggle to efficiently perform underwater, surface, and aerial movement. In this paper, we propose a novel multimodal surfing aquatic-aerial vehicle, SurfAAV, which efficiently integrates underwater n…
The ability of autonomous subtask generation is important for robots to effectively cope with unforeseen situations during indoor search and rescue missions. While prior work mainly focused on improving individual low-level skills of the rescue robot, this paper proposes AutoExpand: a high-level fra
Articulated tracked robots face significant challenges in maintaining stable locomotion over uneven terrain due to unknown contact points between tracks and ground, which are critical for dynamic control. Unlike legged robots, where contact locations can be predicted, tracked systems require real-ti
Point cloud prediction (PCP) aims to forecast future 3D point clouds of scenes by leveraging sequential historical LiDAR scans, offering a promising avenue to enhance the perceptual capabilities of autonomous systems. However, existing methods mostly adopt an end-to-end approach without explicitly m
Despite significant advancements in the research of aquatic-aerial robots, existing configurations struggle to efficiently perform underwater, surface, and aerial movement. In this paper, we propose a novel multimodal surfing aquaticaerial vehicle, SurfAAV, which efficiently integrates underwater na
Open-world Instance Segmentation (OIS) is a challenging task that aims to accurately segment every object instance appearing in the current observation, regardless of whether these instances have been labeled in the training set. This is important for safety-critical applications such as robust auto…
The registration of point clouds is a key ingredient of LiDAR-based SLAM systems and mapping approaches. A challenging task in this context is finding the right data association between 3D points. This paper proposes a novel and flexible graph network architecture to tackle the keypoint matching pro
In this paper, we address the problem of combining 3D laser scanner and camera information to estimate the motion of a mobile platform. We propose a direct laser-visual odometry approach building upon photometric image alignment. Our approach is designed to maximize the information usage of both, th…
Pose estimation and mapping are key capabilities of most autonomous vehicles and thus a number of localization and SLAM algorithms have been developed in the past. Autonomous robots and cars are typically equipped with multiple sensors. Often, the sensor suite includes a camera and a laser range fin…
Extrinsic sensor calibration is an important task in robotics. There are various ways to perform the calibration task, but it often remains unclear which methods are better than the others. In this paper, we provide a systematic study about the calibration accuracy of three types of calibration meth…
Most state estimation procedures in mobile robotics require information about the locations of the individual sensors on the platform. In this paper, we study the motion-based multi-sensor extrinsic calibration problem and point out an overlooked defect of traditional least squares estimation in thi…