DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lim, HyungTae | ko |
dc.contributor.author | Gil, Hyeonjae | ko |
dc.contributor.author | Myung, Hyun | ko |
dc.date.accessioned | 2020-11-30T06:50:18Z | - |
dc.date.available | 2020-11-30T06:50:18Z | - |
dc.date.created | 2020-11-26 | - |
dc.date.created | 2020-11-26 | - |
dc.date.issued | 2020-10-25 | - |
dc.identifier.citation | IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp.10750 - 10757 | - |
dc.identifier.issn | 2153-0858 | - |
dc.identifier.uri | http://hdl.handle.net/10203/277734 | - |
dc.description.abstract | In this study, a deep-learning-based multi-stage network architecture called Multi-Stage Depth Prediction Network (MSDPN) is proposed to predict a dense depth map using a 2D LiDAR and a monocular camera. Our proposed network consists of a multi-stage encoder-decoder architecture and Cross Stage Feature Aggregation (CSFA). The proposed multi-stage encoder-decoder architecture alleviates the partial observation problem caused by the characteristics of a 2D LiDAR, and CSFA prevents the multi-stage network from diluting the features and allows the network to learn the inter-spatial relationship between features better. Previous works use sub-sampled data from the ground truth as an input rather than actual 2D LiDAR data. In contrast, our approach trains the model and conducts experiments with a physically-collected 2D LiDAR dataset. To this end, we acquired our own dataset called KAIST RGBD-scan dataset and validated the effectiveness and the robustness of MSDPN under realistic conditions. As verified experimentally, our network yields promising performance against state-of-the-art methods. Additionally, we analyzed the performance of different input methods and confirmed that the reference depth map is robust in untrained scenarios. | - |
dc.language | English | - |
dc.publisher | IEEE Robotics and Automation Society (RAS) | - |
dc.title | MSDPN: Monocular Depth Prediction with Partial Laser Observation using Multi-stage Neural Networks | - |
dc.type | Conference | - |
dc.identifier.wosid | 000724145800082 | - |
dc.identifier.scopusid | 2-s2.0-85102405423 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 10750 | - |
dc.citation.endingpage | 10757 | - |
dc.citation.publicationname | IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | Virtual | - |
dc.identifier.doi | 10.1109/IROS45743.2020.9340767 | - |
dc.contributor.localauthor | Myung, Hyun | - |
dc.contributor.nonIdAuthor | Gil, Hyeonjae | - |
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