  Ego-pose estimation and dynamic object tracking are two critical problems for
autonomous driving systems. The solutions to these problems are generally based
on their respective assumptions, \ie{the static world assumption for
simultaneous localization and mapping (SLAM) and the accurate ego-pose
assumption for object tracking}. However, these assumptions are challenging to
hold in dynamic road scenarios, where SLAM and object tracking become closely
correlated. Therefore, we propose DL-SLOT, a dynamic LiDAR SLAM and object
tracking method, to simultaneously address these two coupled problems. This
method integrates the state estimations of both the autonomous vehicle and the
stationary and dynamic objects in the environment into a unified optimization
framework. First, we used object detection to identify all points belonging to
potentially dynamic objects. Subsequently, a LiDAR odometry was conducted using
the filtered point cloud. Simultaneously, we proposed a sliding window-based
object association method that accurately associates objects according to the
historical trajectories of tracked objects. The ego-states and those of the
stationary and dynamic objects are integrated into the sliding window-based
collaborative graph optimization. The stationary objects are subsequently
restored from the potentially dynamic object set. Finally, a global pose-graph
is implemented to eliminate the accumulated error. Experiments on KITTI
datasets demonstrate that our method achieves better accuracy than SLAM and
object tracking baseline methods. This confirms that solving SLAM and object
tracking simultaneously is mutually advantageous, dramatically improving the
robustness and accuracy of SLAM and object tracking in dynamic road scenarios.
