2017/05/17 by Jing Dong, Dong, Jing, Byron Boots +3 · 1 citation
Computer Science · Engineering · #FOS: Computer and information sciences #Image and Object Detection Techniques #Robotics (cs.RO) #Robotics and Sensor-Based Localization #Target Tracking and Data Fusion in Sensor Networks
paper · pdf · doi:10.48550/arxiv.1705.06020
openalex publication_date 2017/05/17 · openalex created_date 2017/05/26 · openalex updated_date 2026/07/28
Continuous-time trajectory representations are a powerful tool that can be used to address several issues in many practical simultaneous localization and mapping (SLAM) scenarios, like continuously collected measurements distorted by robot motion, or during with asynchronous sensor measurements. Sparse Gaussian processes (GP) allow for a probabilistic non-parametric trajectory representation that enables fast trajectory estimation by sparse GP regression. However, previous approaches are limited to dealing with vector space representations of state only. In this technical report we extend the work by Barfoot et al. [1] to general matrix Lie groups, by applying constant-velocity prior, and defining locally linear GP. This enables using sparse GP approach in a large space of practical SLAM settings. In this report we give the theory and leave the experimental evaluation in future publications.