Navigation in agricultural fields imposes various constraints on manoeuvrability, which can be tackled by using four-wheel steering (4WS) vehicles which are capable of switching between multiple steering mechanisms with distinct kinematic properties. For example, parallel positive steering (PPS) with four wheels in parallel to each other can maintain the vehicle’s heading when moving along a curve. Symmetric negative steering (SNS) with two wheels on each side sharing the same steering angle can turn with a small radius. This paper presents a controller capable of selecting and switching between the two aforementioned modes autonomously for better trajectory tracking performance with special heading requirements for agricultural applications. The controller is implemented as a Model Predictive Control (MPC) controller formulated as a mixed-integer quadratic programming (MIQP) problem for the 4WS vehicle. Practical constraints, such as limits on wheel velocities, steering angles and their rate-of-changes are taken into account. A Python implementation confirms the real-time execution capability of the controller and simulation results highlight its effectiveness.

We present a novel framework for rigid point cloud registration. Our approach is based on the principles of mechanics and thermodynamics. We solve the registration problem by assuming point clouds as rigid bodies consisting of particles. Forces can be applied between both particle systems so that they attract or repel each other. These forces are used to cause rigid-body motion of one particle system toward the other, until both are aligned. The framework supports physics-based registration processes with arbitrary driving forces, depending on the desired behaviour. Additionally, the approach handles feature-enhanced point clouds, e.g., by colours or intensity values. Our framework is freely accessible for download. In contrast to already existing algorithms, our contribution is to precisely register high-resolution point clouds with nearly constant computational effort and without the need for pre-processing, sub-sampling or pre-alignment. At the same time, the quality is up to 28 percent higher than for state-of-the-art algorithms and up to 49 percent higher when considering feature-enhanced point clouds. Even in the presence of noise, our registration approach is one of the most robust, on par with state-of-the-art implementations.

In cranial radiotherapy highly accurate tumor localization is required to spare healthy tissue while delivering sufficient dose to the target. Here, marker-less optical tracking can support immobilization devices and monitor head motion. However, recent studies have shown that the spatial registration of the optically generated point cloud to a reference is not robust enough. It may suffer from misalignments due to local similarities and changes along the deformable surface geometry. Irrespective from the registration algorithm, we propose to support the spatial information by tissue thickness patterns across the surface. These patterns are predicted by Gaussian Processes which process near infrared optical backscatter from the skin. We first demonstrate for a cohort of 30 volunteers that the tissue thickness can be determined with errors of less than 0.22mm on average. We found high prediction accuracy irrespective of gender, age or skin type. Second, we show that the robustness of a standard iterative closest point (ICP) algorithm can be improved when exploiting cutaneous patterns to identify point-to-point correspondences. On average, tissue thickness support outperformed the standard procedure for every subject and was capable of pushing 90% of all misalignments exceeding a registration error of 1mm below that threshold.

Marker-less tracking provides a non-invasive as well as comfortable approach to compensate for head motion in high precision radiotherapy. However, it suffers from a lack of point-to-point correspondences, typically requiring characteristic spatial landmarks to match point clouds. In this study, we show that cutaneous and subcutaneous structures can be uncovered using an 850 nm laser setup. For three subjects, we compare features extracted from camera images with MR scans serving as an anatomical ground truth. The results confirm the validity of the optically detected structures. The negative correlation between skin thickness and reflected light energy is likewise predicted by Monte-Carlo simulations and can be used to improve spatial point cloud matching. Tissue thickness and its facial structure can be predicted with submillimeter accuracy using a Support Vector regression machine. In addition, the optical measurements reveal the location of vessels that are not immediately visible in the MR scan. These promising findings highly encourage its application for a marker-less tracking system.