ATOM Calibration Framework: Interaction and Visualization Functionalities

Robotic systems are evolving to include a large number of sensors and diverse sensor modalities. In order to operate a system with multiple sensors, the geometric transformations between those sensors must be accurately estimated. The process by which these transformations are estimated is known as sensor calibration. Behind every sensor calibration approach is a formulation and a framework. The formulation is the method by which the transformations are estimated. The framework is the set of operations required to carry out the calibration procedure. This paper proposes a novel calibration framework that gives more flexibility, control and information to the user, enhancing the user interface and the user experience of calibrating a robotic system. The framework consists of several visualization and interaction functionalities useful for a calibration procedure, such as the estimation of the initial pose of the sensors, the data collection and labeling, the data review and correction and the visualization of the estimation of the extrinsic and intrinsic parameters. This framework is supported by the Atomic Transformations Optimization Method formulation, referred to as ATOM. Results show that this framework is applicable to various robotic systems with different configurations, number of sensors and sensor modalities. In addition to this, a survey comparing the frameworks of different calibration approaches shows that ATOM provides a very good user experience.

Optimization-Based Online Initialization and Calibration of Monocular Visual-Inertial Odometry Considering Spatial-Temporal Constraints

The online system state initialization and simultaneous spatial-temporal calibration are critical for monocular Visual-Inertial Odometry (VIO) since these parameters are either not well provided or even unknown. Although impressive performance has been achieved, most of the existing methods are designed for filter-based VIOs. For the optimization-based VIOs, there is not much online spatial-temporal calibration method in the literature. In this paper, we propose an optimization-based online initialization and spatial-temporal calibration method for VIO. The method does not need any prior knowledge about spatial and temporal configurations. It estimates the initial states of metric-scale, velocity, gravity, Inertial Measurement Unit (IMU) biases, and calibrates the coordinate transformation and time offsets between the camera and IMU sensors. The work routine of the method is as follows. First, it uses a time offset model and two short-term motion interpolation algorithms to align and interpolate the camera and IMU measurement data. Then, the aligned and interpolated results are sent to an incremental estimator to estimate the initial states and the spatial–temporal parameters. After that, a bundle adjustment is additionally included to improve the accuracy of the estimated results. Experiments using both synthetic and public datasets are performed to examine the performance of the proposed method. The results show that both the initial states and the spatial-temporal parameters can be well estimated. The method outperforms other contemporary methods used for comparison.

Multi-Sensor Extrinsic Calibration Using an Extended Set of Pairwise Geometric Transformations

Systems composed of multiple sensors for exteroceptive perception are becoming increasingly common, such as mobile robots or highly monitored spaces. However, to combine and fuse those sensors to create a larger and more robust representation of the perceived scene, the sensors need to be properly registered among them, that is, all relative geometric transformations must be known. This calibration procedure is challenging as, traditionally, human intervention is required in variate extents. This paper proposes a nearly automatic method where the best set of geometric transformations among any number of sensors is obtained by processing and combining the individual pairwise transformations obtained from an experimental method. Besides eliminating some experimental outliers with a standard criterion, the method exploits the possibility of obtaining better geometric transformations between all pairs of sensors by combining them within some restrictions to obtain a more precise transformation, and thus a better calibration. Although other data sources are possible, in this approach, 3D point clouds are obtained by each sensor, which correspond to the successive centers of a moving ball its field of view. The method can be applied to any sensors able to detect the ball and the 3D position of its center, namely, LIDARs, mono cameras (visual or infrared), stereo cameras, and TOF cameras. Results demonstrate that calibration is improved when compared to methods in previous works that do not address the outliers problem and, depending on the context, as explained in the results section, the multi-pairwise technique can be used in two different methodologies to reduce uncertainty in the calibration process.