arXiv subjects

Xiyuan Liu

Publications and source records attributed to Xiyuan Liu.

24 records · Page 2Linked to original sources

arXiv2022

Efficient and Probabilistic Adaptive Voxel Mapping for Accurate Online LiDAR Odometry

This paper proposes an efficient and probabilistic adaptive voxel mapping method for LiDAR odometry. The map is a collection of voxels; each contains one plane (or edge) feature that enables the probabilistic representation of the environment and accurate registration of a new LiDAR scan. We further analyze the need for coarse-to-fine voxel mapping and then use a novel voxel map organized by a Hash table and octrees to build and update the map efficiently. We apply the proposed voxel map to an iterated extended Kalman filter and construct a maximum a posteriori probability problem for pose estimation. Experiments on the open KITTI dataset show the high accuracy and efficiency of our method compared to other state-of-the-art methods. Outdoor experiments on unstructured environments with non-repetitive scanning LiDARs further verify the adaptability of our mapping method to different environments and LiDAR scanning patterns. Our codes and dataset are open-sourced on Github

cs.RO↗

arXiv2022

FAST-LIVO: Fast and Tightly-coupled Sparse-Direct LiDAR-Inertial-Visual Odometry

To achieve accurate and robust pose estimation in Simultaneous Localization and Mapping (SLAM) task, multi-sensor fusion is proven to be an effective solution and thus provides great potential in robotic applications. This paper proposes FAST-LIVO, a fast LiDAR-Inertial-Visual Odometry system, which builds on two tightly-coupled and direct odometry subsystems: a VIO subsystem and a LIO subsystem. The LIO subsystem registers raw points (instead of feature points on e.g., edges or planes) of a new scan to an incrementally-built point cloud map. The map points are additionally attached with image patches, which are then used in the VIO subsystem to align a new image by minimizing the direct photometric errors without extracting any visual features (e.g., ORB or FAST corner features). To further improve the VIO robustness and accuracy, a novel outlier rejection method is proposed to reject unstable map points that lie on edges or are occluded in the image view. Experiments on both open data sequences and our customized device data are conducted. The results show our proposed system outperforms other counterparts and can handle challenging environments at reduced computation cost. The system supports both multi-line spinning LiDARs and emerging solid-state LiDARs with completely different scanning patterns, and can run in real-time on both Intel and ARM processors. We open source our code and dataset of this work on Github to benefit the robotics community.

cs.RO↗

arXiv2021

Pixel-level Extrinsic Self Calibration of High Resolution LiDAR and Camera in Targetless Environments

In this letter, we present a novel method for automatic extrinsic calibration of high-resolution LiDARs and RGB cameras in targetless environments. Our approach does not require checkerboards but can achieve pixel-level accuracy by aligning natural edge features in the two sensors. On the theory level, we analyze the constraints imposed by edge features and the sensitivity of calibration accuracy with respect to edge distribution in the scene. On the implementation level, we carefully investigate the physical measuring principles of LiDARs and propose an efficient and accurate LiDAR edge extraction method based on point cloud voxel cutting and plane fitting. Due to the edges' richness in natural scenes, we have carried out experiments in many indoor and outdoor scenes. The results show that this method has high robustness, accuracy, and consistency. It can promote the research and application of the fusion between LiDAR and camera. We have open-sourced our code on GitHub to benefit the community.

cs.RO↗

arXiv2020

A decentralized framework for simultaneous calibration, localization and mapping with multiple LiDARs

LiDAR is playing a more and more essential role in autonomous driving vehicles for objection detection, self localization and mapping. A single LiDAR frequently suffers from hardware failure (e.g., temporary loss of connection) due to the harsh vehicle environment (e.g., temperature, vibration, etc.), or performance degradation due to the lack of sufficient geometry features, especially for solid-state LiDARs with small field of view (FoV). To improve the system robustness and performance in self-localization and mapping, we develop a decentralized framework for simultaneous calibration, localization and mapping with multiple LiDARs. Our proposed framework is based on an extended Kalman filter (EKF), but is specially formulated for decentralized implementation. Such an implementation could potentially distribute the intensive computation among smaller computing devices or resources dedicated for each LiDAR and remove the single point of failure problem. Then this decentralized formulation is implemented on an unmanned ground vehicle (UGV) carrying 5 low-cost LiDARs and moving at $1.3m/s$ in urban environments. Experiment results show that the proposed method can successfully and simultaneously estimate the vehicle state (i.e., pose and velocity) and all LiDAR extrinsic parameters. The localization accuracy is up to 0.2% on the two datasets we collected. To share our findings and to make contributions to the community, meanwhile enable the readers to verify our work, we will release all our source codes and hardware design blueprint on our Github.

cs.RO↗

arXiv2019

Bird Flocking Inspired Control Strategy for Multi-UAV Collective Motion

UAV collective motion has become a hot research topic in recent years. The realization of UAV collective motion, however, relied heavily on centralized control method and suffered from instability. Inspired by bird flocking theory, a control strategy for UAV collective motion with distributed measure and control methods was proposed in this study. In order to appropriately adjust the inter-agent distance suitable for realization, the control law based on bird flocking theory was optimized, and the convergence of velocities and collision avoidance properties were presented through simulation results. Furthermore, the stable collective motion of two UAVs using visual relative information only with proposed strategy in both indoor and outdoor GPS-denied environments were realized.

eess.SY↗

arXiv2009

A simple integrated single-atom detector

We present a reliable and robust integrated fluorescence detector capable of detecting single atoms. The detector consists of a tapered lensed single-mode fiber for precise delivery of excitation light and a multimode fiber to collect the fluorescence. Both are mounted in lithographically defined SU-8 holding structures on an atom chip. Rb87 atoms propagating freely in a magnetic guide are detected with an efficiency of up to 66%, and a signal-to-noise ratio in excess of 100 is obtained for short integration times.

physics.atom-ph↗