In the previous chapter, we explored the application of reinforcement
learning to autonomous robots, focusing on the indoor maps constructed using the
Simultaneous Localization and Mapping (SLAM) technique. Visual SLAM (VSLAM)
is highlighted as a cost-effective SLAM system that leverages 3D vision to execute
location and mapping functions without limitations on distance detection range.
VSLAM can also incorporate inertial measurement unit (IMU) measurements to
enhance the accuracy of the device's pose estimation, particularly in scenarios where
visual data alone is insufficient, such as during rapid movements or temporary visual
obstructions. This chapter shifts the focus to integrating deep learning (DL) with
VSLAM to boost its accuracy and performance. DL can significantly enhance VSLAM
by providing semantic understanding, object detection, and loop closure detection,
improving the system's overall situational awareness. We delve into six DL models that
are pivotal in advancing VSLAM capabilities: Convolutional Neural Networks
(CNNs), Long Short-Term Memory (LSTM) networks, Neural Networks (NNs), Graph
Convolutional Networks (GCNs), Message Passing Neural Networks (MPNNs), and
Graph Isomorphism Networks (GINs). Each of these models offers unique advantages
for VSLAM. CNNs are adept at processing visual information and extracting spatial
features, while LSTMs excel in handling temporal dependencies, making them suitable
for dynamic environments. NNs provide a flexible framework for various learning
tasks, and GCNs effectively capture spatial relationships in graph-structured data.
MPNNs and GINs enhance the ability to process and analyze complex graph-based
data, improving the robot's understanding of its environment. This chapter provides a
comprehensive overview of how these DL models can be integrated with VSLAM to
achieve more robust and efficient autonomous navigation. Through detailed
explanations and practical examples, we illustrate the potential of combining DL with
VSLAM to advance the field of autonomous robotics.
