  Current techniques in Visual Simultaneous Localization and Mapping (VSLAM)
estimate camera displacement by comparing image features of consecutive scenes.
These algorithms depend on scene continuity, hence requires frequent camera
inputs. However, processing images frequently can lead to significant memory
usage and computation overhead. In this study, we introduce SemanticSLAM, an
end-to-end visual-inertial odometry system that utilizes semantic features
extracted from an RGB-D sensor. This approach enables the creation of a
semantic map of the environment and ensures reliable camera localization.
SemanticSLAM is scene-agnostic, which means it doesn't require retraining for
different environments. It operates effectively in indoor settings, even with
infrequent camera input, without prior knowledge. The strength of SemanticSLAM
lies in its ability to gradually refine the semantic map and improve pose
estimation. This is achieved by a convolutional long-short-term-memory
(ConvLSTM) network, trained to correct errors during map construction. Compared
to existing VSLAM algorithms, SemanticSLAM improves pose estimation by 17%. The
resulting semantic map provides interpretable information about the environment
and can be easily applied to various downstream tasks, such as path planning,
obstacle avoidance, and robot navigation. The code will be publicly available
at https://github.com/Leomingyangli/SemanticSLAM
