Each map point is defined as a single textured surfel that moves independently of the other map points. In this paper, we introduce a novel deformable camera tracking method with a local deformation model for each point. These template-based methods use an underlying global deformation model. Current approaches use a template-based deformable tracking to recover the camera pose and the deformation of the map. Keywords: SLAM, Localization, Computer Vision for Medical RoboticsĪbstract: Deformable Monocular SLAM algorithms recover the localization of a camera in an unknown deformable environment. Universidad De Zaragoza (VAT: ESQ5018001G) Experiments using 3D kinect data as well as sparse indoor/outdoor LiDAR data show that our method is capable of efficiently producing accurate pose uncertainty estimates. The method provides a non-parametric estimate of the transformation, can model complex multi-modal distributions, and can be effectively parallelized on a GPU. We develop a Stein variational inference framework with gradient based optimization of ICP's cost function. In this work we propose a new algorithm to align two point clouds that can precisely estimate the uncertainty of ICP's transformation parameters. Current probabilistic ICP methods usually do not capture all sources of uncertainty and may provide unreliable transformation estimates which can have a detrimental effect in state estimation or decision making tasks that use this information. However, for safety critical problems such as autonomous driving, a point estimate of the pose transformation is not sufficient as it does not provide information about the multiple solutions. There are many sources of uncertainty in this process that may arise due to sensor noise, ambiguous environment, initial condition, and occlusion. Iterative closest point (ICP) is a commonly used pose estimation algorithm which provides a point estimate of the transformation between two point clouds. Keywords: SLAM, Sensor Fusion, Perception for Grasping and ManipulationĪbstract: Quantification of uncertainty in point cloud matching is critical in many tasks such as pose estimation, sensor fusion, and grasping. Stein ICP for Uncertainty Estimation in Point Cloud Matching We additionally demonstrate state-of-the-art performance of lidar-only odometry with and without using Doppler velocity measurements in nominal conditions. Our algorithm outperforms the only existing method that also uses Doppler velocity measurements, and we study difficult conditions where including this extra information greatly improves performance. We evaluate our proposed algorithm on several real-world datasets, including publicly available ones and datasets we collected. We apply an existing continuous-time framework that efficiently estimates the vehicle trajectory using Gaussian process regression to compensate for motion distortion due to the scanning-while-moving nature of any mechanically actuated lidar (FMCW and non-FMCW). In this letter, we present the first continuous-time lidar-only odometry algorithm using these Doppler velocity measurements from an FMCW lidar to aid odometry in geometrically degenerate environments. Keywords: SLAM, Localization, Range SensingĪbstract: Frequency-Modulated Continuous-Wave (FMCW) lidar is a recently emerging technology that additionally enables per-return instantaneous relative radial velocity measurements via the Doppler effect.
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