Real-Time Semantic Mapping for Autonomous Off-Road Navigation
| Autor: | Daniel Maturana, Po-Wei Chou, Masashi Uenoyama, Sebastian Scherer |
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| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry Terrain 02 engineering and technology Planner Convolutional neural network Mobile robot navigation 020901 industrial engineering & automation Semantic mapping 0202 electrical engineering electronic engineering information engineering Grid reference 020201 artificial intelligence & image processing Segmentation Computer vision Artificial intelligence Image sensor business computer computer.programming_language |
| Zdroj: | Field and Service Robotics ISBN: 9783319673608 FSR |
| Popis: | In this paper we describe a semantic mapping system for autonomous off-road driving with an All-Terrain Vehicle (ATVs). The system’s goal is to provide a richer representation of the environment than a purely geometric map, allowing it to distinguish, e.g., tall grass from obstacles. The system builds a 2.5D grid map encoding both geometric (terrain height) and semantic information (navigation-relevant classes such as trail, grass, etc.). The geometric and semantic information are estimated online and in real-time from LiDAR and image sensor data, respectively. Using this semantic map, motion planners can create semantically aware trajectories. To achieve robust and efficient semantic segmentation, we design a custom Convolutional Neural Network (CNN) and train it with a novel dataset of labelled off-road imagery built for this purpose. We evaluate our semantic segmentation offline, showing comparable performance to the state of the art with slightly lower latency. We also show closed-loop field results with an autonomous ATV driving over challenging off-road terrain by using the semantic map in conjunction with a simple path planner. Our models and labelled dataset will be publicly available at http://dimatura.net/offroad. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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