Autonomous driving has seen huge interest and improvements in the last few years. Two important functions of autonomous driving is the depth and visual odometry estimation.Depth estimation refers to determining the distance from the camera to each point in the scene captured by the camera, while the visual odometry refers to estimation of ego motion using images recorded by the camera. The algorithm presented by Zhou et al. [1] is a completely unsupervised algorithm for depth and ego motion estimation. This thesis sets out to minimize ambiguity and enhance performance of the algorithm [1]. The purpose of the mentioned algorithm is to estimate the depth map given an image, from a camera attached to the agent, and the ego motion of the agent, in the case of the thesis, the agent is a vehicle. The algorithm lacks the ability to make predictions in the true scale in both depth and ego motion, said differently, it suffers from ambiguity. Two extensions of the method were developed by changing the loss function of the algorithm and supervising ego motion. Both methods show a remarkable improvement in their performance and reduced ambiguity, utilizing only the ego motion ground data which is significantly easier to access than depth ground truth data
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-205611 |
| Date | January 2024 |
| Creators | Papadopoulos, Kyriakos |
| Publisher | Linköpings universitet, Statistik och maskininlärning |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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