DualQuat-LOAM: LiDAR Odometry and Mapping parametrized on Dual Quaternions

This paper reports on a novel method for LiDAR odometry estimation, which completely parameterizes the system with dual quaternions. To accomplish this, the features derived from the point cloud, including edges, surfaces, and Stable Triangle Descriptor (STD), along with the optimization problem, are expressed in the dual quaternion set. This approach enables the direct combination of translation and orientation errors via dual quaternion operations, greatly enhancing pose estimation, as demonstrated in comparative experiments against other state-of-the-art methods. Our approach reduced drift error compared to other LiDAR-only-odometry methods, especially in scenarios with sharp curves and aggressive movements with large angular displacement. DualQuat-LOAM is benchmarked against several public datasets. In the KITTI dataset it has a translation and rotation error of 0.79% and 0.0039{\deg}/m, with an average run time of 53 ms.

Visual Servoing NMPC Applied to UAVs for Photovoltaic Array Inspection

The photovoltaic (PV) industry is seeing a significant shift toward large-scale solar plants, where traditional inspection methods have proven to be time-consuming and costly. Currently, the predominant approach to PV inspection using unmanned aerial vehicles (UAVs) is based on photogrammetry. However, the photogrammetry approach presents limitations, such as an increased amount of useless data during flights, potential issues related to image resolution, and the detection process during high-altitude flights. In this work, we develop a visual servoing control system applied to a UAV with dynamic compensation using a nonlinear model predictive control (NMPC) capable of accurately tracking the middle of the underlying PV array at different frontal velocities and height constraints, ensuring the acquisition of detailed images during low-altitude flights. The visual servoing controller is based on the extraction of features using RGB-D images and the Kalman filter to estimate the edges of the PV arrays. Furthermore, this work demonstrates the proposal in both simulated and real-world environments using the commercial aerial vehicle (DJI Matrice 100), with the purpose of showcasing the results of the architecture. Our approach is available for the scientific community in: https://github.com/EPVelasco/VisualServoing_NMPC

ViKi-HyCo: A Hybrid-Control approach for complex car-like maneuvers

This paper presents ViKi-HyCo (Visual servoing and Kinematic Hybrid-Controller), an approach that generates the necessary maneuvers for the complex positioning of a non-holonomic mobile robot in outdoor environments, towards a target point based on the object detection, by combining an image based visual servoing (IBVS) and a kinematic controller. The method avoids the problems of the visual servoing controller when it loses the visual object detection features by switching to a kinematic controller. We also present object localization for outdoor environments employing the fusion of LiDAR and RGB-D cameras that estimates the spatial location of a target point for the kinematic controller, and also allows the dynamic calculation of a desired bounding box of the detected object for the calculation of velocities in the visual servoing controller. The presented approach does not require an object tracking algorithm and is applicable to any visually tracking robotic task where its kinematic model is known. The Hybrid-Control presents an error of 0.0428 \pm 0.0467 m in the X-axis and 0.0515 \pm 0.0323 m in the Y-axis at the end of a complete positioning task.

LiLO: Lightweight and low-bias LiDAR Odometry method based on spherical range image filtering

In unstructured outdoor environments, robotics requires accurate and efficient odometry with low computational time. Existing low-bias LiDAR odometry methods are often computationally expensive. To address this problem, we present a lightweight LiDAR odometry method that converts unorganized point cloud data into a spherical range image (SRI) and filters out surface, edge, and ground features in the image plane. This substantially reduces computation time and the required features for odometry estimation in LOAM-based algorithms. Our odometry estimation method does not rely on global maps or loop closure algorithms, which further reduces computational costs. Experimental results generate a translation and rotation error of 0.86\% and 0.0036{\deg}/m on the KITTI dataset with an average runtime of 78ms. In addition, we tested the method with our data, obtaining an average closed-loop error of 0.8m and a runtime of 27ms over eight loops covering 3.5Km.