Continuous-time trajectory estimation is an attractive alternative to discrete-time batch estimation due to the ability to incorporate high-frequency measurements from asynchronous sensors while keeping the number of optimization parameters bounded. Two types of continuous-time estimation have become prevalent in the literature: Gaussian process regression and spline-based estimation. In this paper, we present a direct comparison between these two methods. We first compare them using a simple linear system, and then compare them in a camera and IMU sensor fusion scenario on SE(3) in both simulation and hardware. Our results show that if the same measurements and motion model are used, the two methods achieve similar trajectory accuracy. In addition, if the spline order is chosen so that the degree-of-differentiability of the two trajectory representations match, then they achieve similar solve times as well.
Department of Aeronautics and Astronautics, Massachusetts Institute of Technology（航空航天系，麻省理工学院）；Department of Electrical and Systems Engineering, University of Pennsylvania（电气与系统工程系，宾夕法尼亚大学）
A Comparative Study of Spline-Based Trajectory Reconstruction Methods Across Varying Automatic Vehicle Location Data Densities
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Department of Civil Architectural and Environmental Engineering（土木建筑与环境工程系）；Center for Transportation Research（交通研究中心）
An Optimized Path Planning of Manipulator Using Spline Curves and Real Quantifier Elimination Based on Comprehensive Gröbner Systems
Graduate School of Pure and Applied Sciences, University of Tsukuba（筑波大学文理学研究院）；Faculty of Pure and Applied Sciences, University of Tsukuba（筑波大学文理学院）；Faculty of Library, Information and Media Science, University of Tsukuba（筑波大学图书馆、信息与媒体科学学院）
本文提出利用Comprehensive Gröbner System (CGS)与CGS-QE方法高效求解机械臂逆运动学并认证轨迹解，结合三次样条插值与最短路径算法实现路径与关节配置的优化规划。
CT-UIO: Continuous-Time UWB-Inertial-Odometer Localization Using Non-Uniform B-spline with Fewer Anchors
National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University（湖南大学机器人视觉感知与控制技术国家工程研究中心）；School of Artificial Intelligence, Changsha University of Science and Technology（长沙理工大学人工智能学院）；School of Artificial Intelligence and Robotics, Hunan University（湖南大学人工智能与机器人学院）；State Key Laboratory of Chemistry for NBC Hazards Protection, Beijing（核生化防护化学重点实验室，北京）
RINGO: Real-time Navigation with a Guiding Trajectory for Aerial Manipulators in Unknown Environments
Institute of Robotics and Automatic Information System（机器人与自动信息系统研究所）；College of Artificial Intelligence（人工智能学院）；Tianjin Key Laboratory of Intelligent Robotics（天津市智能机器人重点实验室）；Nankai University（南开大学）；Institute of Intelligence Technology and Robotic Systems（智能技术与机器人系统研究所）；Shenzhen Research Institute of Nankai University（南开大学深圳研究院）
DynFly: Dynamic-Aware Continuous Trajectory Generation for UAV Vision-Language Navigation in Urban Environments
Beijing Institute of Technology（北京理工大学）；Tsinghua University（清华大学）；Dalian University of Technology（大连理工大学）；Xidian University（西安电子科技大学）；Huazhong University of Science and Technology（华中科技大学）
FSDP: Fast and Safe Data-Driven Overtaking Trajectory Planning for Head-to-Head Autonomous Racing Competitions
Department of Control Science and Engineering, Zhejiang University（控制科学与工程系，浙江大学）；Center for Project-Based Learning, D-ITET, ETH Zurich（基于项目的学习中心，苏黎世联邦理工学院）
A Comparative Study of Rapidly-exploring Random Tree Algorithms Applied to Ship Trajectory Planning and Behavior Generation
Autoship Centre for Research-based Innovation (SFI), Norwegian University of Science and Technology (NTNU)（Autoship 基于研究的创新中心（SFI）、挪威科技大学（NTNU））；Det Norske Veritas (DNV)（挪威船级社（DNV））
Observability-Aware Active Calibration of Multi-Sensor Extrinsics for Ground Robots via Online Trajectory Optimization
Shenzhen Key Laboratory of Control Theory and Intelligent Systems（深圳控制理论与智能系统重点实验室）；Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology（广东省全驱动系统控制理论与技术省级重点实验室）；Southern University of Science and Technology（南方科技大学）
SwordRiding: A Unified Navigation Framework for Quadrotors in Unknown Complex Environments via Online Guiding Vector Fields
Pengcheng Laboratory, Shenzhen, Guangdong, China（鹏城实验室，深圳，广东，中国）；School of Automation, Beijing Institute of Technology, Beijing, China（自动化学院，北京理工大学，北京，中国）；School of Artificial Intelligence and Robotics, Hunan University（人工智能与机器人学院，湖南大学）；Department of Control Science and Engineering, Harbin Institute of Technology（控制科学与工程学院，哈尔滨工业大学）；National Key Laboratory of Autonomous Intelligent Unmanned Systems（自主智能无人系统国家重点实验室）；Department of Mechanical and Automation Engineering, the Chinese University of Hong Kong, Hong Kong, China（机械与自动化工程系，香港中文大学，香港，中国）
