| Tartalom: |
http://hdl.handle.net/10890/16965 |
| Archívum: |
Műegyetem Digitális Archívum |
| Gyűjtemény: |
1. Tudományos közlemények, publikációk
Konferenciák gyűjteményei
Conference on BME ZalaZONE, 2022
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| Cím: |
A Raw Fusion Based 3D Object Detector for Pedestrian and Vehicle Position Estimation
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| Létrehozó: |
Csonthó, Mihály
Rövid, András
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| Dátum: |
2022-05-03T10:04:45Z
2022-05-03T10:04:45Z
2022
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| Tartalmi leírás: |
Robust sensing of the environment is an essential and safety-critical part of self-driving vehicles. Most of the algorithms used for this problem employ sensor fusion to increase the reliability of sensing. The most common is the object-level fusion, where separate sensor detections are combined to create an object list. Less common are low-level fusion algorithms that compile their detection list from fused low-level input data. The algorithm presented here uses low-level (raw) fusion and can remarkably improve the detection accuracy and reliability compared to single-camera systems. The proposed detector is aimed for pedestrian detection but is also capable of detecting the position of vehicles or other specific objects even in cases when the number of lidar points representing the object is low.
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| Nyelv: |
angol
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| Formátum: |
application/pdf
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| Azonosító: |
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