Self-Calibration of Mobile Manipulator Kinematic and Sensor Extrinsic Parameters Through Contact-Bas @ICRA-cg8kk

Uploaded May 2018 | Updated September 2026, 2 weeks ago

ICRA 2018 Spotlight Video
Interactive Session Wed PM Pod Q.1
Authors: Limoyo, Oliver; Ablett, Trevor Louis; Maric, Filip; Volpatti, Luke; Kelly, Jonathan
Title: Self-Calibration of Mobile Manipulator Kinematic and Sensor Extrinsic Parameters Through Contact-Based Interaction

Abstract:
We present a novel approach for mobile manipulator self-calibration using contact information. Our method, based on point cloud registration, is applied to estimate the extrinsic transform between a fixed vision sensor mounted on a mobile base and an end effector. Beyond sensor calibration, we demonstrate that the method can be extended to include manipulator kinematic model parameters, which involves a non-rigid registration process. Our procedure uses on-board sensing exclusively and does not rely on any external measurement devices, fiducial markers, or calibration rigs. Further, it is fully automatic in the general case. We experimentally validate the proposed method on a custom mobile manipulator platform, and demonstrate centimetre-level post-calibration accuracy in positioning of the end effector using visual guidance only. We also discuss the stability properties of the registration algorithm, in order to determine the conditions under which calibration is possible.

Self-Calibration of Mobile Manipulator Kinematic and Sensor Extrinsic Parameters Through Contact-Bas

Performance Indicator for Benchmarking Force-Controlled Robots

A Tensegrity-Inspired Compliant 3-DOF Compliant Joint

$Enhancing Underwater Imagery Using Generative Adversarial Networks ICRA 2018 Spotlight Video Interactive Session Thu PM Pod J.7 Authors: Fabbri, Cameron; Islam, Md Jahidul; Sattar, Junaed Title: Enhancing Underwater Imagery Using Generative Adversarial Networks Abstract: Autonomous underwater vehicles (AUVs) rely on a variety of sensors acoustic, inertial and visual for intelligent decision making. Due to its non-intrusive, passive nature, and high information content, vision is an attractive sensing modality, particularly at shallower depths. However, factors such as light refraction and absorption, suspended particles in the water, and color distortion affect the quality of visual data, resulting in noisy and distorted images. AUVs that rely on visual sensing thus face difficult challenges, and consequently exhibit poor performance on vision-driven tasks. This paper proposes a method to improve the quality of visual underwater scenes using Generative Adversarial Networks (GANs), with the goal of improving input to vision-driven behaviors further down the autonomy pipeline. Furthermore, we show how recently proposed methods are able to generate a dataset for the purpose of such underwater image restoration. For any visually-guided underwater robots, this improvement can result in increased safety and reliability through robust visual perception. To that effect, we present quantitative and qualitative data which demonstrates that images corrected through the proposed approach generate more visually appealing images, and also provide increased accuracy for a diver tracking algorithm.$

Real-time Underwater 3D Reconstruction Using Global Context and Active Labeling

Landmark-Based Exploration with Swarm of Resource Constrained Robots

Straight-Leg Walking through Underconstrained Whole-Body Control

Dexterous Manipulation by Two Fingers with Coupled Joints

Integration of Local Geometry and Metric Information in Sampling-Based Motion Planning

PRM-RL: Long-Range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-Based P

Planar Object Tracking in the Wild: A Benchmark

Extending a Dynamic Friction Model with Nonlinear Viscous and Thermal Dependency for a Motor and Har