Name
Abstract | ||
|---|---|---|
Loop closure detection is a crucial module in simultaneous localization and mapping (SLAM), which reduces the accumulative error in building the environment map. Traditional appearance-based methods mostly utilize hand-crafted features, which are designed based on human expertise. Recent advances in deep learning inspire us to investigate its application in loop closure detection. Different from traditional approaches, deep learning methods automatically learn features from raw data and has better adaptability to complex environment changes. In this paper, we perform a comparison and analysis of several popular deep neural networks and traditional methods for loop closure detection. We evaluate their performance on two open datasets in terms of accuracy and processing time. According to the experimental results, we conclude that deep neural network is suitable for loop closure detection. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/iThings-GreenCom-CPSCom-SmartData.2017.18 | 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) |
Keywords | Field | DocType |
Simultaneous Localization and Mapping,Loop Closure Detection,Deep Learning | Adaptability,Pattern recognition,Visualization,Computer science,Feature extraction,Artificial intelligence,Deep learning,Simultaneous localization and mapping,Artificial neural network,Reflection mapping,For loop | Conference |
ISBN | Citations | PageRank |
978-1-5386-3067-9 | 1 | 0.35 |
References | Authors | |
13 | 5 | |