A plethora of state estimation techniques have appeared in the last decade using visual data, and more recently with added inertial data. Datasets typically used for evaluation include indoor and urban environments, where supporting videos have shown impressive performance. However, such techniques have not been fully evaluated in challenging conditions, such as the marine domain. In this paper, we compare ten recent open-source packages to provide insights on their performance and guidelines on addressing current challenges. Specifically, we selected direct methods and tightly-coupled optimization techniques that fuse camera and Inertial Measurement Unit (IMU) data together. Experiments are conducted by testing all packages on datasets collected over the years with underwater robots in our laboratory. All the datasets are made available online.
Realtime Edge Based Inertial Visual Odometry @cite_29 is specifically designed for Micro Aerial Vehicles (MAV). In particular, it tracks the pose of a robot by fusing data from a monocular camera and an IMU. The approach first processes the images to detect edges to track and map. An EKF is used for estimating the depth. The system was evaluated using the EuRoC dataset @cite_33 . The reliance to edge detection resulted in consistent failure over all underwater datasets.
@cite_29 @cite_33
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A working solution for control and teleoperation of Micro Aerial Vehicles using a frontal camera and an inertial measurement unit as sole sensors is presented. The system is an extension of an edge based visual odometry algorithm to integrate inertial sensors. A mixed tightly-loosely coupled approach is used, taking advantage of each sensor in this minimalistic setup, while keeping the complexity low. The system runs completely on board a MAV providing a semidense output that is more useful for navigation than the sparse maps generated by most feature based systems. To the best of the author’s knowledge, the system is the first semidense VO method running fully on board a MAV for vision in the loop control. An extensive evaluation of the method is presented using the EuRoC MAV dataset, that is specially targeted for MAV navigation in realistic situations. Some of the practical issues of teleoperation are also addressed, in particular how data is transmitted and presented to the user. Finally, real life experiments are included to illustrate the performance of the complete system and the teleoperation interface. 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.