6 accepted papers
LiDAR has become an essential sensing modality in autonomous driving, robotics, and smart-city applications. However, ghost points (or ghost), which are false reflections caused by multi-path laser returns from glass and reflective surfaces, severely degrade 3D mapping and localization accuracy. Pri
As autonomous mobile robots increasingly operate in real-world environments, safety has emerged as a critical challenge, particularly regarding obstacle and pedestrian detection in building blind spots and reliable traffic signal recognition. While traditional Vehicle-to-Infrastructure (V2I) systems…
The Segment Anything Model (SAM) has demonstrated strong versatility across various visual tasks. However, its large storage requirements and high computational cost pose challenges for practical deployment. Post-training quantization (PTQ) has emerged as an effective strategy for efficient deployme…
As autonomous mobile robots increasingly operate in real-world environments, safety has emerged as a critical challenge, particularly regarding obstacle and pedestrian detection in building blind spots and reliable traffic signal recognition. While traditional Vehicle-to-Infrastructure (V2I) systems
Accurate localization is essential for enabling modern full self-driving services. These services heavily rely on map-based traffic information to reduce uncertainties in recognizing lane shapes, traffic light locations, and traffic signs. Achieving this level of reliance on map information requires
The quality of robot vision greatly affects the performance of automation systems, where occlusions stand as one of the biggest challenges. If the target is occluded from the sensor, detecting and grasping such objects become very challenging. For example, when multiple robot arms cooperate in a sin…