Autonomous vehicles require intelligent systems to perceive and navigate unstructured envi- ronments. The scope of this project is to improve and develop algorithms and methods to support autonomy in the off-road problem space. This work explores computer vision architectures to support real-time object detection. Furthermore, this project explores multimodal deep fusion and sensor processing for off-road object detection. The networks are compared to and based off of the SqueezeSeg architecture. The MAVS simulator was utilized for data collection and semantic ground truth. The results indicate improvements from the SqueezeSeg performance metrics.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-6105 |
| Date | 30 April 2021 |
| Creators | Foster, Timothy |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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