Summary:
Accurate, efficient, and robust state estimation is more important than ever in robotics as the variety of platforms and complexity of tasks continue to grow. Historically, discrete-time filters and smoothers have been the dominant approach, in which the estimated variables are states at discrete sample times. The paradigm of continuous-time state estimation proposes an alternative strategy by estimating variables that express the state as a continuous function of time, which can be evaluated at any query time. Not only can this benefit downstream tasks such as planning and control, but it also significantly increases estimator performance and flexibility, as well as reduces sensor preprocessing and interfacing complexity. Despite this, continuous-time methods remain underutilized, potentially because they are less well-known within robotics. To remedy this, this work presents a unifying formulation of these methods and the most exhaustive literature review to date, systematically categorizing prior work by methodology, application, state variables, historical context, and theoretical contribution to the field. By surveying splines and Gaussian processes together and contextualizing works from other research domains, this work identifies and analyzes open problems in continuous-time state estimation and suggests new research directions.
Spanish layman's summary:
Este trabajo promueve la estimación de estados en tiempo continuo en robótica como una alternativa más flexible y eficiente a los métodos en tiempo discreto. Presenta un marco unificado y una revisión exhaustiva de investigaciones previas, señala los desafíos abiertos y propone futuras líneas de investigación para avanzar en el campo.
English layman's summary:
This work promotes continuous-time state estimation in robotics as a more flexible and efficient alternative to discrete-time methods. It offers a unifying framework and comprehensive review of prior research, highlights open challenges, and suggests future directions for advancing the field.
Keywords: State Estimation, Continuous-Time, Splines, Gaussian Processes, Optimization, Sensor Fusion
JCR-JIF Impact Factor and WoS quartile: 11,100 - Q1 (2025)
DOI reference:
https://doi.org/10.1109/TRO.2025.3593079
Published on paper: 2025.
Published on-line: July 2025.
Citation:
W. Talbot, J. Nubert, T. Tuna, C. Cadena, F. Dümbgen, J. Tordesillas Torres, "Continuous-Time State Estimation Methods in Robotics: A Survey", IEEE Transactions on Robotics, Vol. 41, pp. 4975 - 4999, 2025. [Online: July 2025] doi: 10.1109/TRO.2025.3593079
