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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 03838naa a2200409 4500 | |
| 001 | oai:research.chalmers.se:2f20480c-9595-435e-9243-7eaec9945f80 | |
| 003 | SwePub | |
| 008 | 181111s2019 | |||||||||||000 ||eng| | |
| 024 | 7 | a https://research.chalmers.se/publication/5059312 URI |
| 024 | 7 | a https://doi.org/10.1016/j.robot.2018.11.0022 DOI |
| 024 | 7 | a https://research.chalmers.se/publication/5066942 URI |
| 040 | a (SwePub)cth | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a art2 swepub-publicationtype |
| 072 | 7 | a ref2 swepub-contenttype |
| 100 | 1 | a Caltagirone, Luca,d 1983u Chalmers tekniska högskola,Chalmers University of Technology4 aut0 (Swepub:cth)lucac |
| 245 | 1 0 | a LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks |
| 264 | 1 | b Elsevier BV,c 2019 |
| 520 | a In this work, a deep learning approach has been developed to carry out road detection by fusing LIDAR point clouds and camera images. An unstructured and sparse point cloud is first projected onto the camera image plane and then upsampled to obtain a set of dense 2D images encoding spatial information. Several fully convolutional neural networks (FCNs) are then trained to carry out road detection, either by using data from a single sensor, or by using three fusion strategies: early, late, and the newly proposed cross fusion. Whereas in the former two fusion approaches, the integration of multimodal information is carried out at a predefined depth level, the cross fusion FCN is designed to directly learn from data where to integrate information; this is accomplished by using trainable cross connections between the LIDAR and the camera processing branches. To further highlight the benefits of using a multimodal system for road detection, a data set consisting of visually challenging scenes was extracted from driving sequences of the KITTI raw data set. It was then demonstrated that, as expected, a purely camera-based FCN severely underperforms on this data set. A multimodal system, on the other hand, is still able to provide high accuracy. Finally, the proposed cross fusion FCN was evaluated on the KITTI road benchmark where it achieved excellent performance, with a MaxF score of 96.03%, ranking it among the top-performing approaches. | |
| 650 | 7 | a NATURVETENSKAPx Data- och informationsvetenskapx Annan data- och informationsvetenskap0 (SwePub)102992 hsv//swe |
| 650 | 7 | a NATURAL SCIENCESx Computer and Information Sciencesx Other Computer and Information Science0 (SwePub)102992 hsv//eng |
| 650 | 7 | a TEKNIK OCH TEKNOLOGIERx Annan teknikx Mediateknik0 (SwePub)211022 hsv//swe |
| 650 | 7 | a ENGINEERING AND TECHNOLOGYx Other Engineering and Technologiesx Media Engineering0 (SwePub)211022 hsv//eng |
| 650 | 7 | a NATURVETENSKAPx Data- och informationsvetenskapx Datorseende och robotik0 (SwePub)102072 hsv//swe |
| 650 | 7 | a NATURAL SCIENCESx Computer and Information Sciencesx Computer Vision and Robotics0 (SwePub)102072 hsv//eng |
| 653 | a fully convolutional neural network | |
| 653 | a autonomous driving | |
| 700 | 1 | a Bellone, Mauro,d 1982u Chalmers tekniska högskola,Chalmers University of Technology4 aut0 (Swepub:cth)bellone |
| 700 | 1 | a Svensson, Lennart,d 1976u Chalmers tekniska högskola,Chalmers University of Technology4 aut0 (Swepub:cth)pale |
| 700 | 1 | a Wahde, Mattias,d 1969u Chalmers tekniska högskola,Chalmers University of Technology4 aut0 (Swepub:cth)mwahde |
| 710 | 2 | a Chalmers tekniska högskola4 org |
| 773 | 0 | t Robotics and Autonomous Systemsd : Elsevier BVg 111, s. 125-131q 111<125-131x 0921-8890 |
| 856 | 4 | u http://arxiv.org/pdf/1809.07941 |
| 856 | 4 8 | u https://research.chalmers.se/publication/505931 |
| 856 | 4 8 | u https://doi.org/10.1016/j.robot.2018.11.002 |
| 856 | 4 8 | u https://research.chalmers.se/publication/506694 |
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