A general framework for solving the subspace clustering problem using the CUR decomposition is presented. The CUR decomposition provides a natural way to construct similarity matrices for data that come from a union of unknown subspaces U = M i=1 S i . The similarity matrices thus constructed give the exact clustering in the noise-free case. Additionally, this decomposition gives rise to many distinct similarity matrices from a given set of data, which allow enough flexibility to perform accurate clustering of noisy data. We also show that two known methods for subspace clustering can be derived from the CUR decomposition. An algorithm based on the theoretical construction of similarity matrices is presented, and experiments on synthetic and real data are presented to test the method.Additionally, an adaptation of our CUR based similarity matrices is utilized to provide a heuristic algorithm for subspace clustering; this algorithm yields the best overall performance to date for clustering the Hopkins155 motion segmentation dataset.

In this paper, the effects of wheel slip estimation and compensation of trajectory tracking for orchard applications were investigated. A slippage estimator was developed and adapted into a car-like robot model. Steering and velocity commands were generated using a model-based control approach. The whole system was implemented and tested on an autonomous orchard vehicle that has steerable front wheels and actuated rear wheels. A high accuracy positioning system was used to estimate the longitudinal and lateral slip velocities while the vehicle is moving. A laser scanning range finder was placed at the front centre of the vehicle, which was used to detect rows of trees in the orchard. Procedures were first tested in a non-flat but open space, which was covered with snow. Then it was tested on an experimental orchard where the surface was covered with heavy mud and the vehicle was expected to follow trajectories that span multiple rows in the orchard. The vehicle detected individual trees as well as rows of trees to track the centre of each row and manoeuvred from one row to the next. The experimental results showed that trajectory tracking performance of the vehicle was enhanced via integrating a slippage estimator into the system model. Furthermore, using the slippage estimation in the system model increased the accuracy, repeatability and performance of the control system. Keywords Autonomous orchard vehicle; Slippage estimation; Row following; Turning. Nomenclature RTK-GPS real time kinematic-Global Positioning System WD wheel drive ROS Robot Operating System x, y coordinate system in the centre of the rear axle of the vehicle X, Y vehicle's current position

In this paper we propose a novel model-based control method for an autonomous agricultural vehicle that operates in tree fruit orchards. The method improves path following performance by taking into account the vehicle's motion model, including the effects of wheel sideslip, to calculate speed and steering commands. It also generates turn paths that improve visibility of the orchard rows, thus increasing the probability of a successful turn from one row into another, while respecting maximum steering rate limits. The method does not depend on GPS signals for either state estimation or path following, relying instead only on data from a planar laser scanner and wheel and steering encoders. This makes it suitable for real agricultural applications where acquisition cost is key to a farmer's decision to invest in new

No abstract