  With the recent advance of deep learning based object recognition and
estimation, it is possible to consider object level SLAM where the pose of each
object is estimated in the SLAM process. In this paper, based on a novel Lie
group structure, a right invariant extended Kalman filter (RI-EKF) for object
based SLAM is proposed. The observability analysis shows that the proposed
algorithm automatically maintains the correct unobservable subspace, while
standard EKF (Std-EKF) based SLAM algorithm does not. This results in a better
consistency for the proposed algorithm comparing to Std-EKF. Finally,
simulations and real world experiments validate not only the consistency and
accuracy of the proposed algorithm, but also the practicability of the proposed
RI-EKF for object based SLAM problem. The MATLAB code of the algorithm is made
publicly available.
