Heiko Bülow

Online generation of an underwater photo map with improved Fourier Mellin based registration

A novel algorithm for stitching images, which is faster and more robust than standard approaches like the Scale Invariant Feature Transform (SIFT) is presented. The algorithm is particularly suited for Autonomous Underwater Vehicles (AUV), namely for the online generation of photo maps that can be the basis of intelligent onboard functionalities. The photo maps can be generated just based on registration, i.e., without any information about the vehicles pose or its motion. A new method based on the Fourier-Mellin (FM) transform is presented. It is based on a 2D Fourier transform where the shift between two signals is detected by the phase information. In contrast to previous work using FM, a polar-logarithmic resampling of image information is used to turn rotation and scaling into corresponding phase shift and allow for a registration in one step.

Spectral registration of noisy sonar data for underwater 3D mapping

Abstract3D mapping is very challenging in the underwater domain, especially due to the lack of high resolution, low noise sensors. A new spectral registration method is presented that can determine the spatial 6 DOF transformation between pairs of very noisy 3D scans with only partial overlap. The approach is hence suited to cope with sonar as the predominant underwater sensor. The spectral registration method is based on Phase Only Matched Filtering (POMF) on non-trivially resampled spectra of the 3D data.Two extensive sets of experiments are presented. First, evaluations with simulated data are done where the type and amount of noise can be controlled and the ground truth transformations between scans are known. Second, real world data from a Tritech Eclipse sonar is used. Concretely, 18 sonar scans of a large structure in form of a flood gate and a lock in the river Lesum in Bremen are used for 3D mapping. In doing so, the spectral registration method is compared to two other methods suited for noisy 3D registrations, namely Iterative Closest Point (ICP) and plane-based registration. It is shown that the spectral registration method performs very well in terms of the resulting 3D map as well as its run-times.

Safety, Security, and Rescue Missions with an Unmanned Aerial Vehicle (UAV)

Several missions with an Unmanned Aerial Vehicle (UAV) in different realistic safety, security, and rescue field tests are presented. First, results from two safety and security missions at the 2009 European Land Robot Trials (ELROB) are presented. A UAV in form of an Airrobot AR100-B is used in a reconnaissance and in a camp security scenario. The UAV is capable of autonomous waypoint navigation using onboard GPS processing. A digital video stream from the vehicle is used to create photo maps—also known as mosaicking—in real time at the operator station. This mapping is done using an enhanced version of Fourier Mellin based registration, which turns out to be very fast and robust. Furthermore, results from a rescue oriented scenario at the 2010 Response Robot Evaluation Exercises (RREE) at Disaster City, Texas are presented. The registration for the aerial mosaicking is supplemented by an uncertainty metric and embedded into Simultaneous Localization and Mapping (SLAM), which further enhances the photo maps as main mission deliveries.

Maximum likelihood mapping with spectral image registration

A core challenge in probabilistic mapping is to extract meaningful uncertainty information from data registration methods. While this has been investigated in ICP-based scan matching methods, other registration methods have not been analyzed. In this paper, an uncertainty analysis of a Fourier Mellin based image registration algorithm is introduced, which to our knowledge is the first of its kind involving spectral registration. A covariance matrix is extracted from the result of a Phase-Only Matched Filter, which is interpreted as a probability mass function. The method is embedded in a pose graph implementation for Simultaneous Localization and Mapping (SLAM) and validated with experiments in the underwater domain.

Fast and robust photomapping with an Unmanned Aerial Vehicle (UAV)

A fast and robust method for visual odometry based on the Fourier-Mellin Invariant (FMI) descriptor is presented. It extends previous FMI based approaches in two ways. First, a logarithmic representation of the spectral magnitude of the FMI descriptor is used. Second, a filter on the frequency where the shift is supposed to appear is applied. It is shown with experiments with an Unmanned Aerial Vehicle that this improved Fourier-Mellin Invariant (iFMI) method is is indeed an advancement and well suited for online visual odometry to generate large photo maps.

Spectral 6DOF Registration of Noisy 3D Range Data with Partial Overlap

We present Spectral Registration with Multilayer Resampling (SRMR) as a 6 Degrees Of Freedom (DOF) registration method for noisy 3D data with partial overlap. The algorithm is based on decoupling 3D rotation from 3D translation by a corresponding resampling process of the spectral magnitude of a 3D Fast Fourier Transform (FFT) calculation on discretized 3D range data. The registration of all 6DOF is then subsequently carried out with spectral registrations using Phase Only Matched Filtering (POMF). There are two main aspects for the fast and robust registration of Euler angles from spherical information in SRMR. First of all, there is the permanent use of phase matching. Second, based on the FFT on a discrete Cartesian grid, not only one spherical layer but also a complete stack of layers are processed in one step. Experiments are presented with challenging datasets with respect to interference and overlap. The results include the fast and robust registration of artificially transformed data for ground-truth comparison, scans from the Stanford Bunny dataset, high end 3D laser range finder (LRF) scans of a city center, and range data from a low-cost actuated LRF in a disaster response scenario.

Uncertainty estimation for a 6-DoF spectral registration method as basis for sonar-based underwater 3D SLAM

An uncertainty estimation method for 6 degree of freedom (6-DoF) spectral registration is introduced here. The underlying 6-DoF registration method based on Phase Only Matched Filtering (POMF) is capable of dealing with very noisy sensor data. It is hence well suited for 3D underwater mapping, where relatively inaccurate sonar imaging devices have to be employed. An uncertainty estimation method is required to use this registration method in a Simultaneous Localization and Mapping (SLAM) framework. To our knowledge, the first such method for 6-DoF spectral registration is presented here. This new uncertainty estimation method treats the POMF results as probability mass functions (PMF). Due to the decoupling in the underlying method, yaw is computed by a one-dimensional POMF leading hence to a 1D PMF; roll and pitch are simultaneously computed and hence encoded in a 2D PMF. Furthermore, a 3D PMF is generated for the translation as it is determined by a 3D POMF. A normal distribution is fitted on each of the PMF to get the uncertainty estimate. The method is experimentally evaluated with simulated as well as real world sonar data. It is shown that it indeed can be used for SLAM, which significantly improves the map quality.

An efficient strategy for data exchange in multi-robot mapping under underwater communication constraints

The online generation of underwater image maps or mosaicking is of high interest for underwater robots, e.g., for autonomous navigation, exploration, or object detection. Here, a cooperative approach is presented that addresses the particular challenges of the severe constraints on communication bandwidth in the underwater domain. Concretely, a special update strategy for a cooperatively maintained pose graph as basis for Simultaneous Localization and Mapping (SLAM) is introduced. The strategy tries to transmit the most relevant information within the limits of the communication bandwidth to maximize the quality of the cooperative map. It is shown in experiments with simulations based on real world data that the strategy leads to near optimal results while obeying the severe bandwidth constraints of realistic underwater communication.

Using Robust Spectral Registration for Scan Matching of Sonar Range Data

Abstract Scan matching is the process of registering two range scans and is usually applied to laser data. However, such precise sensors are unavailable in underwater environments. Specific algorithms to match sonar range scans have been proposed, but require significant interpretative analysis of the scan to extract range readings similar to laser scanners. This paper presents a fast and robust scan matching method based on spectral registration of rendered scan data. Sonar scans do not need to be pre-processed, other than simple thresholding. The method is tested on a publicly available data set recorded in an abandoned marina.

On the effects of Sampling Resolution in Improved Fourier Mellin based Registration for Underwater Mapping

Abstract The properties of the fast and robust method for visual odometry based on the Fourier-Mellin Invariant (FMI) descriptor are analyzed here. The algorithm is particularly suited for Autonomous Underwater Vehicles (AUV), namely for the online generation of photo maps that can be the basis of intelligent on-board functionalities. The photo maps can be generated just based on registration, i.e., without any information about the vehicles pose or its motion. The algorithm makes heavy use of 2D Fourier transformations and its speed thus depends on the resolution of the input images and the size of the intermediate matrices used. Using the ground truth path of generated artificial video streams, the effects of different resolutions and other parameters are compared and evaluated with respect to their speed.