  Classical visual simultaneous localization and mapping (SLAM) algorithms
usually assume the environment to be rigid. This assumption limits the
applicability of those algorithms as they are unable to accurately estimate the
camera poses and world structure in real life scenes containing moving objects
(e.g. cars, bikes, pedestrians, etc.). To tackle this issue, we propose
TwistSLAM: a semantic, dynamic and stereo SLAM system that can track dynamic
objects in the environment. Our algorithm creates clusters of points according
to their semantic class. Thanks to the definition of inter-cluster constraints
modeled by mechanical joints (function of the semantic class), a novel
constrained bundle adjustment is then able to jointly estimate both poses and
velocities of moving objects along with the classical world structure and
camera trajectory. We evaluate our approach on several sequences from the
public KITTI dataset and demonstrate quantitatively that it improves camera and
object tracking compared to state-of-the-art approaches.
