  Background: Loop closure detection is a crucial part in robot navigation and
simultaneous location and mapping (SLAM). Appearance-based loop closure
detection still faces many challenges, such as illumination changes, perceptual
aliasing and increasing computational complexity. Method: In this paper, we
proposed a visual loop-closure detection algorithm which combines illumination
robust descriptor DIRD and odometry information. The estimated pose and
variance are calculated by the visual inertial odometry (VIO), then the loop
closure candidate areas are found based on the distance between images. We use
a new distance combing the the Euclidean distance and the Mahalanobis distance
and a dynamic threshold to select the loop closure candidate areas. Finally, in
loop-closure candidate areas, we do image retrieval with DIRD which is an
illumination robust descriptor. Results: The proposed algorithm is evaluated on
KITTI_00 and EuRoc datasets. The results show that the loop closure areas could
be correctly detected and the time consumption is effectively reduced. We
compare it with SeqSLAM algorithm, the proposed algorithm gets better
performance on PR-curve.
