Object Detection for Semantic SLAM using Convolution Neural Networks
نویسنده
چکیده
Conventional SLAM (Simultaneous Localization and Mapping) systems typically provide odometry estimates and point-cloud reconstructions of an unknown environment. While these outputs can be used for tasks such as autonomous navigation, they lack any semantic information. Our project implements a modular object detection framework that can be used in conjunction with a SLAM engine to generate semantic scene reconstructions. A semantically-augmented reconstruction has many potential applications. Some examples include:
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تاریخ انتشار 2014