Entropy-based sim(3) calibration of 2D lidars to egomotion sensors

Lambert, Jacob; Clement, Lee; Giamou, Matthew; Kelly, Jonathan

doi:10.1109/mfi.2016.7849530

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…During operation, intentional adjustments or unintended mechanical stresses may necessitate recalibration in the field. This need for recalibration outside of a factory or laboratory setting has led to a plethora of automatic calibration methods for a variety of sensor combinations [1]- [4]. These methods operate with varying speed, accuracy, and assumptions about the robot's environment, and also differ in the level of sensor specificity and technician involvement required.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

Certifiably Globally Optimal Extrinsic Calibration From Per-Sensor Egomotion

Giamou

Peretroukhin

et al. 2019

IEEE Robot. Autom. Lett.

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“…Otherwise, many methods use the assumption that LiDAR points are structured on continuous surfaces to construct a metric for point cloud consistency based on mutual information. Point cloud entropy was defined and used to calibrate an array of rotating 2D sensors [35], [36] and also for extrinsic calibration with cameras [37] and egomotion sensors [38]. A similar metric based on the same assumption to approach unsupervised selfcalibration of an HDL-64S2 as an energy optimization problem was also developed [39].…”

Section: A Lidar Testingmentioning

confidence: 99%

Performance Analysis of 10 Models of 3D LiDARs for Automated Driving

et al. 2020

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Automated vehicle technology has recently become reliant on 3D LiDAR sensing for perception tasks such as mapping, localization and object detection. This has led to a rapid growth in the LiDAR manufacturing industry with several competing makers releasing new sensors regularly. With this increased variety of LiDARs, each with different properties such as number of laser emitters, resolution, field-of-view, and price tags, a more in-depth comparison of their characteristics and performance is required. This work compares 10 commonly used 3D LiDARs, establishing several metrics to assess their performance. Various outstanding issues with specific LiDARs were qualitatively identified. The accuracy and precision of individual LiDAR beams and accumulated point clouds are evaluated in a controlled environment at distances from 5 to 180 meters. Reflective targets were used to characterize intensity patterns and quantify the impact of surface reflectivity on accuracy and precision. A vehicle and pedestrian mannequin were also used as additional targets of interest. A thorough assessment of these LiDARs is given with their potential applicability for automated driving tasks. The data collected in these experiments and analysis tools are all shared openly.

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“…One way to compensate for these changes is to employ self-calibration techniques, in which the system calibrates independently using only its on-board hardware. Robust self-calibration has already been demonstrated for certain sensor combinations, including cameras, inertial measurement units, and lidars (e.g., [1]- [4]).…”

Section: Introductionmentioning

confidence: 99%

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

Limoyo¹,

Ablett²,

Marić³

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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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 nonrigid 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.

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Entropy-based sim(3) calibration of 2D lidars to egomotion sensors

Cited by 6 publications

References 24 publications

Certifiably Globally Optimal Extrinsic Calibration From Per-Sensor Egomotion

Certifiably Globally Optimal Extrinsic Calibration From Per-Sensor Egomotion

Performance Analysis of 10 Models of 3D LiDARs for Automated Driving

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

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