Autonomous vehicles use different sensors to estimate their surroundings, one of which is a stereo camera. However, the efficiency of this sensor in unstructured and heterogeneous traffic has not been studied. This paper discusses and evaluates some state-of-the-art depth estimation algorithms alongside traditional stereo-matching algorithms. The algorithms have been evaluated in various weather conditions and times of the day. The environment contained unstructured and heterogeneous traffic elements such as cyclists, dense traffic, two-wheeled vehicles, and random pedestrians. The paper considers different methods for stereo-matching and generating disparity maps. To ensure that the results were produced in diverse scenarios, the stereo depth estimation algorithms were evaluated on ApolloScape, and data was generated from CARLA.
Evaluating Depth Estimation Algorithms in Unstructured Driving Environments
24.09.2023
471683 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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