6 accepted papers
Object goal navigation aims to guide an agent to find a specific target object in an unseen environment using only first-person visual observations. It requires the agent to enhance scene understanding and train a robust navigation policy. To address this, we proposed two complementary techniques, c…
Cooperative localization is essential for swarm applications like collaborative exploration and search-and-rescue missions. However, maintaining real-time capability, robustness, and computational efficiency on resource-constrained platforms presents significant challenges. To address these challeng…
Zero-shot Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires agents to follow natural language instructions and navigate without task-specific training. Prior works have demonstrated the potential of open-source large language models (LLMs) in zero-shot VLN-CE, yet two major…
Object-goal navigation aims to guide an agent to find a specific target object in an unfamiliar environment based on first-person visual observations. It requires the agent to learn informative visual representations and robust navigation policy. To promote these two components, we proposed two comp
Pose estimation is a crucial problem in simultaneous localization and mapping (SLAM). However, developing a robust and consistent state estimator remains a significant challenge, as the traditional extended Kalman filter (EKF) struggles to handle the model nonlinearity, especially for inertial measu
In order to estimate both camera poses and dynamic object poses, the visual SLAMMOT method combines visual Simultaneous Localization and Mapping (SLAM) with Multiple Object Tracking (MOT). Many visual SLAMMOT methods represent dynamic objects as bounding boxes and point cloud clusters, which ignores