1 research outputs found
High-quality Instance-aware Semantic 3D Map Using RGB-D Camera
We present a mapping system capable of constructing detailed instance-level
semantic models of room-sized indoor environments by means of an RGB-D camera.
In this work, we integrate deep-learning-based instance segmentation and
classification into a state of the art RGB-D SLAM system. We leverage the
pipeline of ElasticFusion [1] as a backbone and propose modifications of the
registration cost function. The proposed objective function features a tunable
weight for the appearance channel, which can be learned from data. The
resulting system is capable of producing accurate semantic maps of room-sized
environments, as well as reconstructing highly detailed object-level models.
The developed method has been verified through experimental validation on the
TUMRGB-D SLAM benchmark and the YCB video dataset. Our results confirmed that
the proposed system performs favorably in terms of trajectory estimation,
surface reconstruction, and segmentation quality in comparison to other
state-of-the-art systems