  Determining the relative position and orientation of objects in an
environment is a fundamental building block for a wide range of robotics
applications. To accomplish this task efficiently in practical settings, a
method must be fast, use common sensors, and generalize easily to new objects
and environments. We present MSL-RAPTOR, a two-stage algorithm for tracking a
rigid body with a monocular camera. The image is first processed by an
efficient neural network-based front-end to detect new objects and track 2D
bounding boxes between frames. The class label and bounding box is passed to
the back-end that updates the object's pose using an unscented Kalman filter
(UKF). The measurement posterior is fed back to the 2D tracker to improve
robustness. The object's class is identified so a class-specific UKF can be
used if custom dynamics and constraints are known. Adapting to track the pose
of new classes only requires providing a trained 2D object detector or labeled
2D bounding box data, as well as the approximate size of the objects. The
performance of MSL-RAPTOR is first verified on the NOCS-REAL275 dataset,
achieving results comparable to RGB-D approaches despite not using depth
measurements. When tracking a flying drone from onboard another drone, it
outperforms the fastest comparable method in speed by a factor of 3, while
giving lower translation and rotation median errors by 66% and 23%
respectively.
