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Chapter and Conference Paper
Dual Channel Knowledge Graph Embedding with Ontology Guided Data Augmentation
Current knowledge graph completion suffers from two major issues: data sparsity and false negatives. To address these challenges, we propose an ontology-guided joint embedding framework that utilizes dual data...
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Article
S4-SLAM: A real-time 3D LIDAR SLAM system for ground/watersurface multi-scene outdoor applications
For outdoor ground/watersurface multi-scene applications with sparse feature points, high moving speed and high dynamic noises, a real-time 3D LIDAR SLAM system (S4-SLAM) for unmanned vehicles/ships is propose...
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Article
Grasp Pose Detection with Affordance-based Task Constraint Learning in Single-view Point Clouds
Learning to grasp novel objects is a challenging issue for service robots, especially when the robot is performing goal-oriented manipulation or interaction tasks whilst only single-view RGB-D sensor data is a...
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Chapter and Conference Paper
Stereo Visual SLAM Using Bag of Point and Line Word Pairs
The traditional point-based SLAM algorithm performs poorly due to light changing, low-texture and highly similar scenes, while line segment features can better describe the structural information of the enviro...
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Chapter and Conference Paper
Research and Implementation of Person Tracking Method Based on Multi-feature Fusion
Aiming at the problem of person tracking for mobile robot in complex and dynamic environment, a multi-feature tracking strategy is proposed in this paper, by which the target can be determined based on the jo...
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Article
Robotic Etiquette: Socially Acceptable Navigation of Service Robots with Human Motion Pattern Learning and Prediction
Nonverbal and noncontact behaviors play a significant role in allowing service robots to structure their interactions with humans. In this paper, a novel human-mimic mechanism of robot’s navigational skills wa...
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Chapter and Conference Paper
The High-Activity Parallel Implementation of Data Preprocessing Based on MapReduce
Data preprocessing is an important and basic technique for data mining and machine learning. Due to the dramatic increasing of information, traditional data preprocessing techniques are time-consuming and not ...
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Article
Information-theoretic Approaches Based on Sequential Monte Carlo to Collaborative Distributed Sensors for Mobile Robot Localization
We consider the Sequential Monte Carlo (SMC) method for Bayesian inference applied to the problem of information-theoretic distributed sensor collaboration in complex environments. The robot kinematics and sen...