Search Results for author:
Found 16 papers, 4 papers with code
General Place Recognition Survey: Towards Real-World Autonomy
We conclude with a discussion on PR's future directions and provide a summary of the literature covered at: https://github. com/MetaSLAM/GPRS.
Dual-Enhanced Coreset Selection with Class-wise Collaboration for Online Blurry Class Incremental Learning
To tackle these challenges we introduce DECO (Dual-Enhanced Coreset Selection with Class-wise Collaboration) an approach that starts by establishing a class-wise balanced memory to address data imbalances followed by a tailored class-wise gradient-based similarity scoring system for refined coreset selection strategies with reasonable score guidance to all classes.
MUI-TARE: Multi-Agent Cooperative Exploration with Unknown Initial Position
To intelligently balance the robustness of sub-map merging and exploration efficiency, we develop a new approach for lidar-based multi-agent exploration, which can direct one agent to repeat another agent's trajectory in an \emph{adaptive} manner based on the quality indicator of the sub-map merging process.
General Place Recognition Survey: Towards the Real-world Autonomy Age
A summary of this work and our datasets and evaluation API is publicly available to the robotics community at: https://github. com/MetaSLAM/GPRS.
Loop Closure Detection
Simultaneous Localization and Mapping
+1
BioSLAM: A Bio-inspired Lifelong Memory System for General Place Recognition
We present BioSLAM, a lifelong SLAM framework for learning various new appearances incrementally and maintaining accurate place recognition for previously visited areas.
AutoMerge: A Framework for Map Assembling and Smoothing in City-scale Environments
We present AutoMerge, a LiDAR data processing framework for assembling a large number of map segments into a complete map.
SphereVLAD++: Attention-based and Signal-enhanced Viewpoint Invariant Descriptor
Our previous work provides a viewpoint-invariant descriptor to deal with viewpoint differences; however, the global descriptor suffers from a low signal-noise ratio in unsupervised clustering, reducing the distinguishable feature extraction ability.
Binary Single-dimensional Convolutional Neural Network for Seizure Prediction
Nowadays, several deep learning methods are proposed to tackle the challenge of epileptic seizure prediction.
PanGu-Bot: Efficient Generative Dialogue Pre-training from Pre-trained Language Model
We investigate different aspects of responses generated by PanGu-Bot, including response quality, knowledge, and safety.
An End-to-End Deep Learning Approach for Epileptic Seizure Prediction
Conventional seizure prediction works usually rely on features extracted from Electroencephalography (EEG) recordings and classification algorithms such as regression or support vector machine (SVM) to locate the short time before seizure onset.
A New Neuromorphic Computing Approach for Epileptic Seizure Prediction
Motivated by the energy-efficient spiking neural networks (SNNs), a neuromorphic computing approach for seizure prediction is proposed in this work.
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications
Experiments show that human performance is well above current state-of-the-art baseline systems, leaving plenty of room for the community to make improvements.
