Liang, Liang, W., Wu, Y. (2022). Parameterized Particle Filtering for Tactile-Based Simultaneous Pose and Shape Estimation. IEEE Robotics and Automation Letters, 7(2), 1270–1277. https://doi.org/10.1109/lra.2021.3139381
Abstract:
Object state and shape estimation is essential in many
robotic manipulation tasks (e.g., in-hand manipulation, insertion).
While such estimation is typically relied on visual perception,
for tasks to be carried out in a vision-degraded or vision-denied
environment, haptics becomes the reliable source of perception. In
this letter, we propose the use of parameterized particle filtering to
estimate object pose and shape in 3D space using tactile feedback.
This approach is able to estimate with high accuracy using contact
information of the object with a collision surface from a rough
initial estimation. In comparison to conventional particle filtering,
this approach significantly reduces the number of particles
required for a satisfactory estimation, making it applicable for pose
and shape estimation, where the number of degrees of freedom
is high or even uncertain. Moreover, the proposed method can
automatically choose the fastest-convergent contact action during
the pose estimation stage to shorten the time required. A set of
experiments in both simulation and on a real-world robot have been
conducted to validate the proposed method and compare against
the state-of-the-art approach in the literature. Results from both
sets of experiments show that the proposed method can determine
the pose and shape of the objects with very high accuracy within a
small number of iterations.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Robotics Programme of Singapore - Robotics Enabling Capabilities and Technologies
Grant Reference no. : W2025d0244