Simultaneous Localization, Mapping and Moving Object Tracking

LOCALIZATION, mapping and moving object tracking serve as the basis for scene understanding, which is a key prerequisite for making a robot truly autonomous. Simultaneous localization, mapping and moving object tracking (SLAMMOT) involves not only simultaneous localization and mapping (SLAM) in dynamic environments but also detecting and tracking these dynamic objects. It is believed by many that a solution to the SLAM problem would open up a vast range of potential applications for autonomous robots. Accordingly, a solution to the SLAMMOT problem would expand robotic applications in proximity to human beings where robots work not only for people but also with people. This thesis establishes a new discipline at the intersection of SLAM and moving object tracking. Its contributions are two-fold: theoretical and practical. From a theoretical perspective, we establish a mathematical framework to integrate SLAM and moving object tracking, which provides a solid basis for understanding and solving the whole problem. We describe two solutions: SLAM with generic objects (GO), and SLAM with detection and tracking of moving objects (DATMO). SLAM with GO calculates a joint posterior over all generic objects and the robot. Such an approach is similar to existing SLAM algorithms, but with additional structure to allow for motion modelling of the generic objects. Unfortunately, it is computationally demanding and infeasible. Consequently, we provide the second solution, SLAM with DATMO, in which the estimation problem is decomposed into two separate estimators. By maintaining separate posteriors for the stationary objects and the moving objects, the resulting estimation problems are much lower dimensional than SLAM with GO. From a practical perspective, we develop algorithms for dealing with the implementation issues on perception modelling, motion modelling and data association. Regarding perception modelling, a hierarchical object based representation is presented to integrate existing feature-based, grid-based and direct methods. The samplingand correlationbased range image matching algorithm is developed to tackle the problems arising from uncertain, sparse and featureless measurements. With regard to motion modelling, we describe a move-stop hypothesis tracking algorithm to tackle the difficulties of tracking ground moving objects. Kinematic information from motion modelling as well as geometric information from perception modelling is used to aid data association at different levels. By following the theoretical guidelines and implementing the described algorithms, we are able to demonstrate the feasibility of SLAMMOT using data collected from the Navlab8 and Navlab11 vehicles at high speeds in crowded urban environments.