  Feature descriptors, such as SIFT and ORB, are well-known for their
robustness to illumination changes, which has made them popular for
feature-based<EMAIL_ADDRESS>However, in degraded imaging conditions such as low
light, low texture, blur and specular reflections, feature extraction is often
unreliable. In contrast, direct VSLAM methods which estimate the camera pose by
minimizing the photometric error using raw pixel intensities are often more
robust to low textured environments and blur. Nonetheless, at the core of
direct VSLAM is the reliance on a consistent photometric appearance across
images, otherwise known as the brightness constancy assumption. Unfortunately,
brightness constancy seldom holds in real world applications.
  In this work, we overcome brightness constancy by incorporating feature
descriptors into a direct visual odometry framework. This combination results
in an efficient algorithm that combines the strength of both feature-based
algorithms and direct methods. Namely, we achieve robustness to arbitrary
photometric variations while operating in low-textured and poorly lit
environments. Our approach utilizes an efficient binary descriptor, which we
call Bit-Planes, and show how it can be used in the gradient-based optimization
required by direct methods. Moreover, we show that the squared Euclidean
distance between Bit-Planes is equivalent to the Hamming distance. Hence, the
descriptor may be used in least squares optimization without sacrificing its
photometric invariance. Finally, we present empirical results that demonstrate
the robustness of the approach in poorly lit underground environments.
