IEEE Robotics and Automation Letters · 2019 · 36 citations · 21 references
EngineeringMachine Learning3D Pose EstimationContinuous-time Trajectory EstimationField RoboticsKinesiologyData ScienceMotion CaptureRobot LearningKinematicsHealth SciencesMachine VisionGp PriorMotion SynthesisInverse ProblemsComputer ScienceSimultaneous Trajectory EstimationSignal ProcessingComputer VisionOdometryHuman MovementMotion Analysis
Simultaneous trajectory estimation and mapping (STEAM) offers an efficient approach to continuous-time trajectory estimation, by representing the trajectory as a Gaussian process (GP). Previous formulations of the STEAM framework use a GP prior that assumes white-noise-on-acceleration, with the prior mean encouraging constant body-centric velocity. We show that such a prior cannot sufficiently represent trajectory sections with nonzero acceleration, resulting in a bias to the posterior estimates. This letter derives a novel motion prior that assumes white-noise-on-jerk, where the prior mean encourages constant body-centric acceleration. With the new prior, we formulate a variation of STEAM that estimates the pose, body-centric velocity, and body-centric acceleration. By evaluating across several datasets, we show that the new prior greatly outperforms the white-noise-on-acceleration prior in terms of the solution accuracy.
21
MonoSLAM: Real-Time Single Camera SLAM
Andrew J. Davison, Ian Reid, Nicholas Molton et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2007 · 3.9K citations
LOAM: Lidar Odometry and Mapping in Real-time
Ji Zhang, Sanjiv Singh · 2014 · 3K citations
Continuous 3D scan-matching with a spinning 2D laser
Michael Bosse, Robert Zlot · 2009 · 292 citations