Fast Relative Pose Calibration for Visual and Inertial Sensors

Kelly, Jonathan; Sukhatme, Gaurav S.

doi:10.1007/978-3-642-00196-3_59

Cited by 36 publications

(22 citation statements)

References 10 publications

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“…Because hundreds of features are tracked, and lost, at each time instant, the complexity of the map grows rapidly, and therefore visual features must be organized efficiently in order to enable rapid localization. Many have addressed this issue in the simultaneous localization and mapping (SLAM) community, for instance (Bosse et al, 2004, Eade and Drummond, 2007a, Guivant and Nebot, 2001, Klein and Murray, 2007, Konolige and Agrawal, 2008, Mouragnon et al, 2006, Nebot and Durrant-Whyte, 1999, Kelly and Sukhatme, 2009, Chum et al, 2009 just to mention a few. In section 5.2 we describe our own topological representation that is based on the notion of "locations" defined by co-visibility.…”

Section: Map Building and Localizationmentioning

confidence: 99%

Visual-inertial navigation, mapping and localization: A scalable real-time causal approach

Jones

Soatto

2011

The International Journal of Robotics Research

382

324

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We present a model to estimate motion from monocular visual and inertial measurements. We analyze the model and characterize the conditions under which its state is observable, and its parameters are identifiable. These include the unknown gravity vector, and the unknown transformation between the camera coordinate frame and the inertial unit. We show that it is possible to estimate both state and parameters as part of an on-line procedure, but only provided that the motion sequence is "rich enough," a condition that we characterize explicitly. We then describe an efficient implementation of a filter to estimate the state and parameters of this model, including gravity and camera-to-inertial calibration. It runs in real-time on an embedded platform, and its performance has been tested extensively. We report experiments of continuous operation, without failures, re-initialization, or re-calibration, on paths of length up to 30Km. We also describe an integrated approach to "loop-closure," that is the recognition of previously-seen locations and the topological re-adjustment of the traveled path. It represents visual features relative to the global orientation reference provided by the gravity vector estimated by the filter, and relative to the scale provided by their known position within the map; these features are organized into "locations" defined by visibility constraints, represented in a topological graph, where loop closure can be performed without the need to re-compute past trajectories or perform bundle adjustment. The software infrastructure as well as the embedded platform is described in detail in a technical report (Jones and Soatto (2009).)

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Section: Map Building and Localizationmentioning

confidence: 99%

Visual-inertial navigation, mapping and localization: A scalable real-time causal approach

Jones

Soatto

2011

The International Journal of Robotics Research

382

324

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“…Apart from requiring fewer parameters when fusing measurements of significantly different rates such as images and inertial data, this approach allows for an accurate estimation of the fixed time delay between camera and IMU. Like other frameworks [9], [10], the calibration procedure requires waving the setup in front of a checkerboard, while exciting all rotational degrees of freedom sufficiently in order to render the displacement of camera and IMU well observable. We also experimented with incorporating the calibration for the stereo extrinsics directly into the unified calibration framework, but observed degraded performance when used in visual-inertial SLAM, an explanation to which may be that the setups between calibration and SLAM vary (mostly as far as scene depth is concerned).…”

Section: Calibrationmentioning

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

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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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“…Kelly and Sukhatme present an UKF based calibration algorithm which can be based on artificial or natural landmarks [8], [4]. The idea is to allow a robot to simultaneously explore the environment while calibrating its sensors.…”

Section: Related Workmentioning

confidence: 99%

Optimization based IMU camera calibration

Fleps

Mair

Ruepp

et al. 2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Inertia-visual sensor fusion has become popular due to the complementary characteristics of cameras and IMUs. Once the spatial and temporal alignment between the sensors is known, the fusion of measurements of these devices is straightforward. Determining the alignment, however, is a challenging problem. Especially the spatial translation estimation has turned out to be difficult, mainly due to limitations of camera dynamics and noisy accelerometer measurements. Up to now, filtering-based approaches for this calibration problem are largely prevalent. However, we are not convinced that calibration, as an offline step, is necessarily a filtering issue, and we explore the benefits of interpreting it as a batch-optimization problem. To this end, we show how to model the IMU-camera calibration problem in a nonlinear optimization framework by modeling the sensors' trajectory, and we present experiments comparing this approach to filtering and system identification techniques. The results are based both on simulated and real data, showing that our approach compares favorably to conventional methods.

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Fast Relative Pose Calibration for Visual and Inertial Sensors

Cited by 36 publications

References 10 publications

Visual-inertial navigation, mapping and localization: A scalable real-time causal approach

Visual-inertial navigation, mapping and localization: A scalable real-time causal approach

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

Optimization based IMU camera calibration

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