  Conventional SLAM techniques strongly rely on scene rigidity to solve data
association, ignoring dynamic parts of the scene. In this work we present
Semi-Direct DefSLAM (SD-DefSLAM), a novel monocular deformable SLAM method able
to map highly deforming environments, built on top of DefSLAM. To robustly
solve data association in challenging deforming scenes, SD-DefSLAM combines
direct and indirect methods: an enhanced illumination-invariant Lucas-Kanade
tracker for data association, geometric Bundle Adjustment for pose and
deformable map estimation, and bag-of-words based on feature descriptors for
camera relocation. Dynamic objects are detected and segmented-out using a CNN
trained for the specific application domain. We thoroughly evaluate our system
in two public datasets. The mandala dataset is a SLAM benchmark with
increasingly aggressive deformations. The Hamlyn dataset contains
intracorporeal sequences that pose serious real-life challenges beyond
deformation like weak texture, specular reflections, surgical tools and
occlusions. Our results show that SD-DefSLAM outperforms DefSLAM in point
tracking, reconstruction accuracy and scale drift thanks to the improvement in
all the data association steps, being the first system able to robustly perform
SLAM inside the human body.
