20 accepted papers
Hybrid representation Visual Simultaneous Localization and Mapping (VSLAM) systems combine the inherent strengths of both discrete and field representations. They promise high-precision tracking and photo-realistic dense mapping. However, current keyframe selection methods in hybrid representation V…
This paper presents FLAF, a focal line and feature-constrained active view planning method for autonomous orientation adjustment of a rotatable active camera during mobile robot navigation. FLAF is built on a visual teach-and-repeat (VT&R) system, which enables robots to cruise various paths that fu
Accurate indoor location-based services are important for mobile robots, especially in complex indoor environments. In this paper, we propose a heterogeneous graph network-based ultra-wide band (UWB) localization method to provide accurate and robust localization results for mobile robots in complex
Robotic manipulation in dynamic environments often requires seamless transitions between different grasp types to maintain stability and efficiency. However, achieving smooth and adaptive grasp transitions remains a challenge, particularly when dealing with external forces and complex motion constra
Although the LiDAR SLAM technique has been already widely deployed on various robots, it may still suffers from degeneracy caused by inadequate constraints in scenes with sparse geometric features. If the degeneracy is not detected and properly processed, the accuracy of localization and mapping wil
Keyframe selection plays a crucial role in balancing computational efficiency and localization accuracy in Visual Simultaneous Localization and Mapping (VSLAM) systems. Existing keyframe selection methods often struggle to capture high-level semantic information in environments where multiple semant
Limited by the working principles, LiDAR-SLAM systems suffer from the degeneration phenomenon in environments such as long corridors and tunnels, due to the lack of sufficient geometric features for frame-to-frame matching. The accuracy and sensitivity of existing degeneracy detection methods need t…
Robot person following (RPF) is a crucial capability in human-robot interaction (HRI) applications, allowing a robot to persistently follow a designated person. In practical RPF scenarios, the person can often be occluded by other objects or people. Consequently, it is necessary to re-identify the p
Large-scale point cloud consists of a multitude of individual objects, thereby encompassing rich structural and underlying semantic contextual information, resulting in a challenging problem in efficiently segmenting a point cloud. Most existing researches mainly focus on capturing intricate local f…
Mobile robots have become more and more popular in large-scale and crowded environments, such as airports, shopping malls, etc. However, due to sparse landmarks and crowd noise, localization in this environment is a great challenge. Furthermore, it is unreliable for the robot to navigate safely in c
As the basics of Visual Simultaneous Localization And Mapping (VSLAM), keyframes play an essential role. In previous works, keyframes are selected according to a series of view change-based strategies for short-term data association (STDA). However, the texture enrichment of frames is always ignored…
Assistive robot systems have been developed to help people accomplish daily manipulation tasks especially for those with disabilities, where scene understanding plays a crucial role in enabling robots to interpret the surroundings and behave accordingly. Most of the current systems approach scene un…
Autonomous mobile manipulation robots that can collect trolleys are widely used to liberate human resources and fight epidemics. Most prior robotic trolley collection solutions only detect trolleys with 2D poses or are merely based on spe-cific marks and lack the formal design of planning algorithms…
Recent works on semantic Simultaneous Localization and Mapping (SLAM) utilizing object landmarks have shown superiority in terms of robustness and accuracy in tracking and localization. 3D object landmarks represented by a cubic or quadric surface are inferred from 2D object bounding boxes which are…
Keypoint matching is an important operation in computer vision and its applications such as visual simultaneous localization and mapping (SLAM) in robotics. This matching operation heavily depends on the descriptors of the keypoints, and it must be performed reliably when images undergo conditional…
Keypoint matching is an important operation in computer vision and its applications such as visual simultaneous localization and mapping (SLAM) in robotics. This matching operation heavily depends on the descriptors of the keypoints, and it must be performed reliably when images undergo condition ch…
Motivated by the need to improve the performance of visual loop closure verification via multi-view geometry (MVG) under significant illumination and viewpoint changes, we propose a keypoint matching method that uses landmarks as an intermediate image representation in order to leverage the power of…
For VSLAM (Visual Simultaneous Localization and Mapping), localization is a challenging task, especially for some challenging situations: textureless frames, motion blur, etc. To build a robust exploration and localization system in a given space, a submap-based VSLAM system is proposed in this pape…
For VSLAM (Visual Simultaneous Localization and Mapping), localization is a challenging task, especially for some challenging situations: textureless frames, motion blur, etc. To build a robust exploration and localization system in a given space, a submap-based VSLAM system is proposed in this pape