Robotics and Autonomous Systems
DenseSLAM: Simultaneous Localization and Dense Mapping
International Journal of Robotics Research
A Comparative Study of Three Mapping Methodologies
Journal of Intelligent and Robotic Systems
Simultaneous Localization, Mapping and Moving Object Tracking
International Journal of Robotics Research
What can be done with an embedded stereo-rig in urban environments?
Robotics and Autonomous Systems
Model based vehicle detection and tracking for autonomous urban driving
Autonomous Robots
A laser-scanner-based approach toward driving safety and traffic data collection
IEEE Transactions on Intelligent Transportation Systems
Describing human-object interaction in intelligent space
HSI'09 Proceedings of the 2nd conference on Human System Interactions
Laser-based detection and tracking moving objects using data-driven Markov chain Monte Carlo
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Trajectory-oriented EKF-SLAM using the fourier-mellin transform applied to microwave radar images
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Low cost IMU-Odometer-GPS ego localization for unusual maneuvers
Information Fusion
Inferring laser-scan matching uncertainty with conditional random fields
Robotics and Autonomous Systems
Bearing similarity measures for self-organizing feature maps
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
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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. 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 sampling- and correlation-based 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.