This paper presents visual-inertial datasets collected on-board a micro aerial vehicle. The datasets contain synchronized stereo images, IMU measurements and accurate ground truth. The first batch of datasets facilitates the design and evaluation of visual-inertial localization algorithms on real flight data. It was collected in an industrial environment and contains millimeter accurate position ground truth from a laser tracking system. The second batch of datasets is aimed at precise 3D environment reconstruction and was recorded in a room equipped with a motion capture system. The datasets contain 6D pose ground truth and a detailed 3D scan of the environment. Eleven datasets are provided in total, ranging from slow flights under good visual conditions to dynamic flights with motion blur and poor illumination, enabling researchers to thoroughly test and evaluate their algorithms. All datasets contain raw sensor measurements, spatio-temporally aligned sensor data and ground truth, extrinsic and intrinsic calibrations and datasets for custom calibrations.

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.

An increasing number of robotic systems feature multiple inertial measurement units (IMUs). Due to competing objectives-either desired vicinity to the center of gravity when used in controls, or an unobstructed field of view when integrated in a sensor setup with an exteroceptive sensor for ego-motion estimation-individual IMUs are often mounted at considerable distance. As a result, they sense different accelerations when the platform is subjected to rotational motions. In this work, we derive a method for spatially calibrating multiple IMUs in a single estimator based on the open-source camera/IMU calibration toolbox kalibr. We further extend the toolbox to determine IMU intrinsics, enabling accurate calibration of low-cost IMUs. The results suggest that the extended estimator is capable of precisely determining these intrinsics and even of localizing individual accelerometer axes inside a commercial grade IMU to millimeter precision.

Abstract-This work presents a small-scale Unmanned Aerial System (UAS) capable of performing inspection tasks in enclosed industrial environments. Vehicles with such capabilities have the potential to reduce human involvement in hazardous tasks and can minimize facility outage periods. The results presented generalize to UAS exploration tasks in almost any GPS-denied indoor environment. The contribution of this work is twofold. First, results from autonomous flights inside an industrial boiler of a coal-fired thermal power plant are presented. A lightweight, vision-aided inertial navigation system provides reliable state estimates under difficult environmental conditions typical of such sites. It relies solely on measurements from an onboard MEMS inertial measurement unit and a pair of cameras arranged in a classical stereo configuration. A model-predictive controller allows for efficient trajectory following and enables flight in close proximity with the boiler surface. As a second contribution, we highlight ongoing developments by displaying state estimation and structure recovery results acquired with an integrated visual-inertial sensor that will be employed on future aerial service robotic platforms. A tight integration in hardware facilitates spatial and temporal calibration of the different sensors and thus enables more accurate and robust ego-motion estimates. Comparison with ground truth obtained from a laser tracker shows that such a sensor can provide motion estimates with drift rates of a only few cm over the period of a typical flight.

In this paper, we consider power spectral density estimation of bandlimited, wide-sense stationary signals from sub-Nyquist sampled data. This problem has recently received attention from within the emerging field of cognitive radio for example, and solutions have been proposed that use ideas from compressed sensing and the theory of digital alias-free signal processing. Here we develop a compressed sensing based technique that employs multi-coset sampling and produces multi-resolution power spectral estimates at arbitrarily low average sampling rates. The technique applies to spectrally sparse and nonsparse signals alike, but we show that when the widesense stationary signal is spectrally sparse, compressed sensing is able to enhance the estimator. The estimator does not require signal reconstruction and can be directly obtained from a straightforward application of nonnegative least squares.

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In this paper, we present a framework for 6D absolute scale motion and structure estimation of a multi-camera system in challenging indoor environments. It operates in real-time and employs information from two cameras with non-overlapping fields of view. Monocular Visual Odometry supplying up-to-scale 6D motion information is carried out in each of the cameras, and the metric scale is recovered via a linear solution by imposing the known static transformation between both sensors. The redundancy in the motion estimates is finally exploited by a statistical fusion to an optimal 6D metric result. The proposed technique is robust to outliers and able to continuously deliver a reasonable measurement of the scale factor. The quality of the framework is demonstrated by a concise evaluation on indoor datasets, including a comparison to accurate ground truth data provided by an external motion tracking system.

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