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.
Methods: In this paper, we proposed a visual loop closure detection algorithm that combines illumination
robust descriptor DIRD and odometry information. In this algorithm, a new distance function
is built by fusing the Euclidean distance function and Mahalanobis distance function, which integrates
the pose uncertainty of body and can dynamically adjust the threshold of potential loop closure
locations. Then, potential locations are verified by calculating the similarity of DIRD descriptors.
Results: The proposed algorithm is evaluated on KITTI and EuRoC datasets, and is compared with
SeqSLAM algorithm, which is one of the state of the art loop closure detection algorithms. The results
show that the proposed algorithm could effectively reduce the computing time and get better
performance on P-R curve.
Conclusion: The new loop closure detection method makes full use of odometry information and
image appearance information. The application of the new distance function can effectively reduce
the missed detection caused by odometry error accumulation. The algorithm does not require extracting
image features or learning stage, and can realize real-time detection and run on the platform
with limited computational power.