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

Limoyo, Oliver; Ablett, Trevor; Marić, Filip; Volpatti, Luke; Kelly, Jonathan

doi:10.1109/icra.2018.8460658

Cited by 9 publications

(4 citation statements)

References 29 publications

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“…Certain bespoke algorithms involving specific sensor pairs and environmental features can be solved in closed form with a minimal set of measurements [11], [12], but these methods require a local optimization when noise is present. The approach for calibrating extrinsic sensor parameters relative to a manipulator base in [13] relies on solving a point cloud registration problem. While there are globally optimal branchand-bound (BnB) algorithms that can guarantee a solution to point cloud registration up to a desired accuracy [14], these techniques can be extremely slow.…”

Section: A Globally Optimal Calibrationmentioning

confidence: 99%

Certifiably Globally Optimal Extrinsic Calibration From Per-Sensor Egomotion

Giamou

Peretroukhin

et al. 2019

IEEE Robot. Autom. Lett.

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We present a certifiably globally optimal algorithm for determining the extrinsic calibration between two sensors that are capable of producing independent egomotion estimates. This problem has been previously solved using a variety of techniques, including local optimization approaches that have no formal global optimality guarantees. We use a quadratic objective function to formulate calibration as a quadratically constrained quadratic program (QCQP). By leveraging recent advances in the optimization of QCQPs, we are able to use existing semidefinite program (SDP) solvers to obtain a certifiably global optimum via the Lagrangian dual problem. Our problem formulation can be globally optimized by existing general-purpose solvers in less than a second, regardless of the number of measurements available and the noise level. This enables a variety of robotic platforms to rapidly and robustly compute and certify a globally optimal set of calibration parameters without a prior estimate or operator intervention. We compare the performance of our approach with a local solver on extensive simulations and multiple real datasets. Finally, we present necessary observability conditions that connect our approach to recent theoretical results and analytically support the empirical performance of our system. Global Optimum minimize R,t JR + Jt, subject to R 2 SO(3) < l a t e x i t s h a 1 _ b a s e 6 4 = " j v M n N v m u H A y R x q A 6 N L e F c X m 9 j 2 c = " > A A A C r H i c b V F d b 9 M w F H X C x 7 b w s T I e e b G o Q E O s U Q L V y t 4 m 8 Y K Q E A P W b l J d V Y 7 j r G a O k 9 n X a C X y j + F n 8 T / 4 A b h p x D b g S J a O z 7 3 n 2 j o 3 q 6 U w k C Q / g / D W 7 T t 3 N z a 3 o n v 3 H z z c 7 j 3 a m Z j K a s b H r J K V P s 2 o 4 V I o P g Y B k p / W m t M y k / w k O 3 + 7 q p 9 8 4 9 q I S h 3 D s u a z k p 4 pz 7 3 n 2 j o 3 q 6 U w k C Q / g / D W 7 T t 3 N z a 3 o n v 3 H z z c 7 j 3 a m Z j K a s b H r J K V P s 2 o 4 V I o P g Y B k p / W m t M y k / w k O 3 + 7 q p 9 8 4 9 q I S h 3 D s u a z k p 4 p U Q h G w U v z 3 o 8 t j D G x K v c 9 H B r y g Y I W l 8 1 n t 0 c m n E G l G 3 C u I c A v o S m F E q X 4 z p 2 L v O s 5 J h e W 5 v j 9 / J r L 4 Z e t c O X d w 4 R E 7 S P t D G O z r 7 6 G o b o x 5 G o C J k J 1 v V 8 + u t 3 X L + a 9 f h I n L f C / J O 1 I H 3 U 4 m v d + k b x i t u Q K m K T G T N O k h l l D N Q g m u Y u I N b y m 7 J y e 8 a m n i p b c z J o 2 S o e f e S X H R a X 9 U Y B b 9 b q j o a U x y z L z n S W F h f m 7 t h L / V 5 t a K N 7 M G q F q C 1 y x 9 U O F l T 4 I v N o L z o X 2 u c i l J 5 R p 4 f + K 2 Y J q y s B v J i K t s b m w w i 9 z I K n P Z 2 A W l Q Z m w c T + 5 t q U Q L V y t 4 m 8 Y K Q E A P W b l J d V Y 7 j r G a O k 9 n X a C X y j + F n 8 T / 4 A b h p x D b g S J a O z 7 3 n 2 j o 3 q 6 U w k C Q / g / D W 7 T t 3 N z a 3 o n v 3 H z z c 7 j 3 a m Z j K a s b H r J K V P s 2 o 4 V I o P g Y B k p / W m t M y k / w k O 3 + 7 q p 9 8 4 9 q I S h 3 D s u a z k p 4 p U Q h G w U v z 3 o 8 t j D G x K v c 9 H B r y g Y I W l 8 1 n t 0 ...

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Section: A Globally Optimal Calibrationmentioning

confidence: 99%

Certifiably Globally Optimal Extrinsic Calibration From Per-Sensor Egomotion

Giamou

Peretroukhin

et al. 2019

IEEE Robot. Autom. Lett.

Self Cite

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“…It is claimed that while pair-wise calibration can lead to inconsistencies, calibrating everything together in a "mutually supportive way" is most efficient. Limoyo et al [34] used contact constraint from sliding on a surface together with RGB-D camera information to formulate a selfcalibration problem for a mobile manipulator to estimate camera extrinsic parameters and manipulator joint angle biases. The former part is also experimentally verified.…”

Section: Related Workmentioning

confidence: 99%

“…Second, only the case where all chains were combined together using a single cost function was considered. In [34], self-observation and contact information are combined but the results have a proof-of-concept character. Our work is inspired by the simulation study of Stepanova et al [35] but presents a completely new setting and results on a different platform.…”

Section: Related Workmentioning

confidence: 99%

Automatic self-contained calibration of an industrial dual-arm robot with cameras using self-contact, planar constraints, and self-observation

Stepanova,

Rozlivek,

Puciow

et al. 2020

Preprint

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We present a robot kinematic calibration method that combines complementary calibration approaches: self-contact, planar constraints, and self-observation. We analyze the estimation of the end effector parameters, joint offsets of the manipulators, calibration of the complete kinematic chain (DH parameters), and we compare our results with ground truth measurements provided by a laser tracker. Our main findings are: (1) When applying the complementary calibration approaches in isolation, the self-contact approach yields the best and most stable results. (2) All combinations of more than one approach were always superior to using any single approach in terms of calibration errors as well as the observability of the estimated parameters. Combining more approaches delivers robot parameters that better generalize to the parts of workspace not used for the calibration. (3) Sequential calibration, i.e. calibrating cameras first and then robot kinematics, is more effective than simultaneous calibration of all parameters. In real experiments, we employ two industrial manipulators mounted on a common base. The manipulators are equipped with force/torque sensors at their wrists, with two cameras attached to the robot base, and with special end effectors with fiducial markers. We collect a new comprehensive dataset for robot kinematic calibration and make it publicly available. The dataset and its analysis provide quantitative and qualitative insights that go beyond the specific manipulators used in this work and are applicable to self-contained robot kinematic calibration in general.

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“…The LiDAR motions are estimated by an ICP algorithm and camera motions are computed by feature point matching. Limoyo et al [ 18 ] proposed the self-calibration of a mobile manipulator by aligning two point clouds from a depth camera and a contact sensor using an ICP-based algorithm.…”

Section: Related Workmentioning

confidence: 99%

Stereo Camera Head-Eye Calibration Based on Minimum Variance Approach Using Surface Normal Vectors

Lee¹,

Jeong

2018

Sensors

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This paper presents a stereo camera-based head-eye calibration method that aims to find the globally optimal transformation between a robot’s head and its eye. This method is highly intuitive and simple, so it can be used in a vision system for humanoid robots without any complex procedures. To achieve this, we introduce an extended minimum variance approach for head-eye calibration using surface normal vectors instead of 3D point sets. The presented method considers both positional and orientational error variances between visual measurements and kinematic data in head-eye calibration. Experiments using both synthetic and real data show the accuracy and efficiency of the proposed method.

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

Cited by 9 publications

References 29 publications

Certifiably Globally Optimal Extrinsic Calibration From Per-Sensor Egomotion

Certifiably Globally Optimal Extrinsic Calibration From Per-Sensor Egomotion

Automatic self-contained calibration of an industrial dual-arm robot with cameras using self-contact, planar constraints, and self-observation

Stereo Camera Head-Eye Calibration Based on Minimum Variance Approach Using Surface Normal Vectors

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