  Most real-time autonomous robot applications require a robot to traverse
through a dynamic space for a long time. In some cases, a robot needs to work
in the same environment. Such applications give rise to the problem of a
life-long SLAM system. Life-long SLAM presents two main challenges i.e. the
tracking should not fail in a dynamic environment and the need for a robust and
efficient mapping strategy. The system should update maps with new information;
while also keeping track of older observations. But, mapping for a long time
can require higher computational requirements. In this paper, we propose a
solution to the problem of life-long SLAM. We represent the global map as a set
of rasterized images of local maps along with a map management system
responsible for updating local maps and keeping track of older values. We also
present an efficient approach of using the bag of visual words method for loop
closure detection and relocalization. We evaluate the performance of our system
on the KITTI dataset and an indoor dataset. Our loop closure system reported
recall and precision of above 90 percent. The computational cost of our system
is much lower as compared to state-of-the-art methods. Our method reports lower
computational requirements even for long-term operation.
