  Structure from Motion (SfM) techniques are being increasingly used to create
3D maps from images in many domains including environmental monitoring.
However, SfM techniques are often confounded in visually repetitive
environments as they rely primarily on globally distinct image features.
Simultaneous Localization and Mapping (SLAM) techniques offer a potential
solution in visually repetitive environments since they use local feature
matching, but SLAM approaches work best with wide-angle cameras that are often
unsuitable for documenting the environmental system of interest. We resolve
this issue by proposing a dual-camera SLAM approach that uses a forward facing
wide-angle camera for localization and a downward facing narrower angle,
high-resolution camera for documentation. Video frames acquired by the forward
facing camera video are processed using a standard SLAM approach providing a
trajectory of the imaging system through the environment which is then used to
guide the registration of the documentation camera images. Fragmentary maps,
initially produced from the documentation camera images via monocular SLAM, are
subsequently scaled and aligned with the localization camera trajectory and
finally subjected to a global optimization procedure to produce a unified,
refined map. An experimental comparison with several state-of-the-art SfM
approaches shows the dual-camera SLAM approach to perform better in repetitive
environmental systems based on select samples of ground control point markers.

Robotics;Computer Vision and Pattern Recognition