6 accepted papers
Reliable depth perception is critical for robotic manipulation, especially for non-Lambertian objects such as transparent or highly specular surfaces, where raw depth measurements are often corrupted or missing. These failures frequently propagate to motion planning, resulting in invalid grasp poses
Few-shot Class Incremental Learning (FSCIL) aims to incrementally learn new classes with limited examples while retaining knowledge of previously learned classes. Recent advancements in prompt tuning for large pre-trained models have shown promise in FSCIL. However, current FSCIL methods still suffe…
Detecting moving events produced by moving objects is a crucial task in the realms of autonomous driving and mobile robots. Moving objects have the potential to create ghost artifacts in mapped environments and pose risks to autonomous navigation. LiDAR serves as a vital sensor for autonomous system…
Large-scale 3D mapping is an important task for robotics and autonomous driving. However, mobile robots and autonomous vehicles with limited hardware resources may face issues with large memory consumption. It is challenging to achieve a balance between mapping quality and memory consumption. To add
3D point cloud maps are widely used in robotic tasks like localization and planning. However, dynamic objects, such as cars and pedestrians, can introduce ghost artifacts during the map generation process, leading to reduced map quality and hindering normal robot navigation. Online dynamic object re
Object detection using an oriented bounding box (OBB) can better target rotated objects by reducing the overlap with background areas. Existing OBB approaches are mostly built on horizontal bounding box detectors by introducing an additional angle dimension optimized by a distance loss. However, as…