Chen, Y, Zhao, L, Lee, KMB, Yoo, C, Huang, S & Fitch, R 2020, 'Broadcast Your Weaknesses: Cooperative Active Pose-Graph SLAM for Multiple Robots', IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 2200-2207.View/Download from: Publisher's site
Zhao, J, Huang, S, Zhao, L, Chen, Y & Luo, X 2019, 'Conic Feature Based Simultaneous Localization and Mapping in Open Environment via 2D Lidar', IEEE Access, vol. 7, pp. 173703-173718.View/Download from: UTS OPUS or Publisher's site
© 2013 IEEE. The conventional planar scan matching approach cannot cope well with the open environment as lacking of sufficient edges and corners. This paper presents a conic feature based simultaneous localization and mapping (SLAM) algorithm via 2D lidar which can adapt to an open environment nicely. The novelty of this work includes threefold: (1) defining a conic feature based parametrization approach; (2) developing a method to utilize feature's conic geometric information and odometry information since open environments are short of regular linear geometric features; (3) developing a factor graph based framework which can be adapted with the proposed parametrization. Simulation experiments and real environment experiments demonstrated that the proposed SLAM algorithm can get accurate and convincing results for the open environment and the map in our representation can express accurately the environment situation.
Chen, Y, Huang, S, Fitch, R & Yu, J 2018, 'Efficient Active SLAM Based on Submap Joining, Graph Topology and Convex Optimization', International Conference on Robotics and Automation, Brisbane, QLD, Australia.View/Download from: UTS OPUS or Publisher's site
The active SLAM problem considered in this paper aims to plan a robot trajectory for simultaneous localization and mapping (SLAM) as well as for an area coverage task with robot pose uncertainty. Based on a model predictive control (MPC) framework, these two problems are solved respectively by different methods. For the uncertainty minimization MPC problem, based on the graphical structure of the 2D feature-based SLAM, a non-convex constrained least-squares problem is presented to approximate the original problem. Then, using variable substitutions, it is further transformed into a convex problem, and then solved by a convex optimization method. For the coverage task considering robot pose uncertainty, it is formulated and solved by the MPC framework and the sequential quadratic programming (SQP) method. In the whole process, considering the computation complexity, we use linear SLAM, which is a submap joining approach, to reduce the time for planning and estimation. Finally, various simulations are presented to validate the effectiveness of the proposed approach.
Chen, Y, Huang, S, Fitch, R & Yu, J 2017, 'Efficient Active SLAM based on Submap Joining', Proc. of ARAA ACRA, Australasian Conference on Robotics and Automation, ARAA, Sydney, Australia, pp. 1-7.View/Download from: UTS OPUS
This paper considers the active SLAM problem
where a robot is required to cover a given area while
at the same time performing simultaneous localization
and mapping (SLAM) for understanding the
environment and localizing the robot itself. We propose
a model predictive control (MPC) framework,
and the minimization of uncertainty in SLAM and
coverage problems are solved respectively by the
Sequential Quadratic Programming (SQP) method.
Then, a decision making process is used to control
the switching of two control inputs. In order to reduce
the estimation and planning time, we use Linear
SLAM, which is a submap joining approach.
Simulation results are presented to validate the effectiveness
of the proposed active SLAM strategy.