Optimization based IMU camera calibration

Fleps, Michael; Mair, Elmar; Ruepp, Oliver; Suppa, Michael; Burschka, Darius

doi:10.1109/iros.2011.6095067

Cited by 55 publications

(24 citation statements)

References 14 publications

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“…As heterogeneous sensor systems for motion estimation and localization become increasingly popular, spatial calibration has attracted some attention and resulted in a variety of frameworks [8]- [10]. More recently, the importance of accurate synchronization of the sensors became apparent and was addressed in [8], [11], [12].…”

Section: Related Workmentioning

confidence: 99%

“…More recently, the importance of accurate synchronization of the sensors became apparent and was addressed in [8], [11], [12]. While this work makes use of the calibration presented in [12] to determine the transformation between cameras and IMU and to determine fixed delays present when polling inertial data, its approach to the problem is exactly antithetic: rather than connecting a set of stand-alone sensors to a general purpose computer and calibrating for potentially time-variant time-offsets afterwards, we pursued a tight integration of all hardware components with a central unit capable of concurrent triggering and polling of all sensors.…”

Section: Related Workmentioning

confidence: 99%

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A synchronized visual-inertial sensor system with FPGA pre-processing for accurate real-time SLAM

Nikolic

Rehder

Burri

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

230

153

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Abstract-Robust, accurate pose estimation and mapping at real-time in six dimensions is a primary need of mobile robots, in particular flying Micro Aerial Vehicles (MAVs), which still perform their impressive maneuvers mostly in controlled environments. This work presents a visual-inertial sensor unit aimed at effortless deployment on robots in order to equip them with robust real-time Simultaneous Localization and Mapping (SLAM) capabilities, and to facilitate research on this important topic at a low entry barrier.Up to four cameras are interfaced through a modern ARM-FPGA system, along with an Inertial Measurement Unit (IMU) providing high-quality rate gyro and accelerometer measurements, calibrated and hardware-synchronized with the images. This facilitates a tight fusion of visual and inertial cues that leads to a level of robustness and accuracy which is difficult to achieve with purely visual SLAM systems. In addition to raw data, the sensor head provides FPGA-pre-processed data such as visual keypoints, reducing the computational complexity of SLAM algorithms significantly and enabling employment on resource-constrained platforms.Sensor selection, hardware and firmware design, as well as intrinsic and extrinsic calibration are addressed in this work. Results from a tightly coupled reference visual-inertial SLAM framework demonstrate the capabilities of the presented system.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A synchronized visual-inertial sensor system with FPGA pre-processing for accurate real-time SLAM

Nikolic

Rehder

Burri

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

230

153

View full text Add to dashboard Cite

show abstract

“…Furgale et al [2] formally derive the general continuous-time SLAM problem and demonstrate the use of uniformly spaced temporal basis functions to model the state in a typical camera-IMU calibration problem. The works by Bibby and Reid [10] and Fleps et al [11] also exhibit the use of uniformly spaced B-splines to parameterize the robot trajectory in a global frame. Anderson and Barfoot [12] also leverage this parametric approach to continuous-time estimation, but derive the relative formulation of the problem, similar to the discrete-time work of Sibley et al [13].…”

Section: Related Workmentioning

confidence: 99%

A hierarchical wavelet decomposition for continuous-time SLAM

Anderson

Dellaert

Barfoot

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-This paper proposes using hierarchical wavelets as a basis in parametric continuous-time batch estimation. The need for a continuous-time robot pose in the simultaneous localization and mapping (SLAM) problem has arisen as stateof-the-art batch SLAM algorithms attempt to handle more challenging hardware; specifically, the continuous-time framework is particularly beneficial when using high-rate sensors, multiple unsynchronized sensors, or scanning sensors, such as lidar and rolling-shutter cameras, during motion. Although the traditional discrete-time SLAM formulation can be adapted by using temporal pose interpolation, approaches using the continuous-time framework are able to generate smooth robot trajectories with less state variables. In this paper, we focus on the parametric approach using temporal basis functions to develop a finite-element representation of the continuous-time robot trajectory. While the majority of current implementations have utilized a uniformly spaced B-spline basis, we note that trajectory richness is often quite variable; in this paper, we show how a hierarchical system of wavelet basis functions can be used to increase the resolution of the solution only in the temporally local regions of the trajectory that require additional detail. We validate our approach by contrasting uniform Bsplines and wavelets in a six-dimensional pose-graph SLAM experiment, using both simulated and real data.

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“…Indeed, this result allows us to calibrate the IMU on the pan-tilt unit. Most recently, calibration approaches using a passive complementary filter on SO(3) [18] or explicitly modeling the trajectory of the sensors to obtain the calibration through batch optimization [19] have emerged.…”

Section: Related Workmentioning

confidence: 99%

Automatic and self-contained calibration of a multi-sensorial humanoid's upper body

Birbach

Bäuml²,

Frese

2012

2012 IEEE International Conference on Robotics and Automation

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Abstract-Complex manipulation tasks require an accurate interplay of actuation and sensing. This accuracy can only be achieved by calibrating the relevant components beforehand. Typically calibration procedures are time-consuming and often include subsequent calibration steps, involve multiple people and require external tools. In this paper we alleviate these issues by auto-calibrating the different sensors of DLR's humanoid Rollin' Justin in a single, completely automatic and selfcontained procedure, i.e. without calibration plate. By observing a single point feature on each wrist while moving the robot's head, the stereo cameras' intrinsic and extrinsic parameters are calibrated together with the arm joint elasticities and joint angle offsets. Additionally, we use the head motion to calibrate an Inertial Measurement Unit (IMU) extrinsically. Parameters are obtained by formulating the calibration problem as a batchoptimization problem that estimates all parameters jointly. A rough initial guess, as is,e.g., available when re-calibrating, is needed for the estimation and to facilitate marker detection. The procedure is validated on real hardware and reduces the effort considerably allowing rapid (5 min movement time), automatic, and accurate calibration by simply "pushing a button".

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Optimization based IMU camera calibration

Cited by 55 publications

References 14 publications

A synchronized visual-inertial sensor system with FPGA pre-processing for accurate real-time SLAM

A synchronized visual-inertial sensor system with FPGA pre-processing for accurate real-time SLAM

A hierarchical wavelet decomposition for continuous-time SLAM

Automatic and self-contained calibration of a multi-sensorial humanoid's upper body

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