Fabio Ramos

The vast amount of data robots can capture today motivates the development of fast and scalable statistical tools to model the space the robot operates in. We devise a new technique for environment representation through continuous occupancy mapping that improves on the popular occupancy grip maps in two fundamental aspects: (1) it does not assume an a priori discrimination of the world into grid cells and therefore can provide maps at an arbitrary resolution; (2) it captures spatial relationships between measurements naturally, thus being more robust to outliers and possessing better generalization performance. The technique, named Hilbert maps, is based on the computation of fast kernel approximations that project the data in a Hilbert space where a logistic regression classifier is learnt. We show that this approach allows for efficient stochastic gradient optimization where each measurement is only processed once during learning in an online manner. We present results with three types of approximations: random Fourier; Nyström; and a novel sparse projection. We also extend the approach to accept probability distributions as inputs, for example, due to uncertainty over the position of laser scans due to sensor or localization errors. In this extended version, experiments were conducted in two dimensions and three dimensions, using popular benchmark datasets. Furthermore, an analysis of the adaptive capabilities of the technique to handle large changes in the data, such as trajectory update before and after loop closure during simultaneous localization and mapping, is also included.

We tackle the problem of multi-task learning with copula process. Multivariable prediction in spatial and spatial-temporal processes such as natural resource estimation and pollution monitoring have been typically addressed using techniques based on Gaussian processes and co-Kriging. While the Gaussian prior assumption is convenient from analytical and computational perspectives, nature is dominated by non-Gaussian likelihoods. Copula processes are an elegant and flexible solution to handle various non-Gaussian likelihoods by capturing the dependence structure of random variables with cumulative distribution functions rather than their marginals. We show how multi-task learning for copula processes can be used to improve multivariable prediction for problems where the simple Gaussianity prior assumption does not hold. Then, we present a transductive approximation for multi-task learning and derive analytical expressions for the copula process model. The approach is evaluated and compared to other techniques in one artificial dataset and two publicly available datasets for natural resource estimation and concrete slump prediction.

We present a distributed anytime algorithm for performing MAP inference in graphical models. The problem is formulated as a linear programming relaxation over the edges of a graph. The resulting program has a constraint structure that allows application of the Dantzig-Wolfe decomposition principle. Subprograms are defined over individual edges and can be computed in a distributed manner. This accommodates solutions to graphs whose state space does not fit in memory. The decomposition master program is guaranteed to compute the optimal solution in a finite number of iterations, while the solution converges monotonically with each iteration. Formulating the MAP inference problem as a linear program allows additional (global) constraints to be defined; something not possible with message passing algorithms. Experimental results show that our algorithm's solution quality outperforms most current algorithms and it scales well to large problems.

This paper presents a framework for integrating sensor information from an inertial measuring unit (IMU), global positioning system (GPS) receiver, and monocular vision camera mounted to a low‐flying unmanned aerial vehicle (UAV) for producing large‐scale terrain reconstructions and classifying different species of vegetation within the environment. The reconstruction phase integrates all of the sensor information using a statistically optimal nonlinear least‐squares bundle adjustment algorithm to estimate vehicle poses simultaneously to a highly detailed point feature map of the terrain. The classification phase uses feature descriptors based on the color and texture properties of vegetation observed in the vision data and uses the terrain information to build a georeferenced map of different types of vegetation. The resulting system can be used for a range of environmental monitoring missions such as invasive plant detection and biomass mapping. Experimental results of the algorithms are demonstrated in a “weed‐finding” mission over a large farmland area of the Australian outback.

Building a model of large‐scale terrain that can adequately handle uncertainty and incompleteness in a statistically sound way is a challenging problem. This work proposes the use of Gaussian processes as models of large‐scale terrain. The proposed model naturally provides a multiresolution representation of space, incorporates and handles uncertainties aptly, and copes with incompleteness of sensory information. Gaussian process regression techniques are applied to estimate and interpolate (to fill gaps in occluded areas) elevation information across the field. The estimates obtained are the best linear unbiased estimates for the data under consideration. A single nonstationary (neural network) Gaussian process is shown to be powerful enough to model large and complex terrain, effectively handling issues relating to discontinuous data. A local approximation method based on a “moving window” methodology and implemented using k‐dimensional (KD)‐trees is also proposed. This enables the approach to handle extremely large data sets, thereby completely addressing its scalability issues. Experiments are performed on large‐scale data sets taken from real mining applications. These data sets include sparse mine planning data, which are representative of a global positioning system–based survey, as well as dense laser scanner data taken at different mine sites. Further, extensive statistical performance evaluation and benchmarking of the technique has been performed through cross‐validation experiments. They conclude that for dense and/or flat data, the proposed approach will perform very competitively with grid‐based approaches using standard interpolation techniques and triangulated irregular networks using triangle‐based interpolation techniques; for sparse and/or complex data, however, it would significantly outperform them.

This paper addresses the problem of denoising aerial gamma‐ray surveying in mining exploration. Conventional methods for denoising spectral data make strong assumptions about the levels and type of noise which reduces their efficiency. The proposed methodology cast the problem as manifold learning followed by non‐linear regression. The model makes no assumptions about the level and type of noise and performs significantly better than previous techniques on both synthetic and real data.

In this paper, simultaneous localisation and mapping (SLAM) is combined with landmark recognition to close large loops in unstructured, outdoor environments. Camera and laser information are fused to recognise and create appearance models for landmarks. The representation is obtained through a non-linear probabilistic regression model encoding a neighbourhood preserving dimensionality reduction. A new data association algorithm is proposed where landmarks are associated based on both position and appearance. The resulting system is more robust and able to recover from possible misassociations. Experiments demonstrate the benefits of this approach in challenging problems involving mapping with large loop closings in irregular terrain, and with dynamic objects.

This paper presents an algorithm for recognition and segmentation of natural features in unstructured environments. By providing a Bayesian solution for the density estimation problem, the algorithm needs significantly less training data than conventional techniques and is applicable to different environments. The algorithm is based on colour and wavelet convolution of image patches to model the information contained in natural features. Dimensionality reduction techniques are applied to map data points to a lower dimensional space where Bayesian density estimation is computed. Experiments were performed in underwater, aerial and terrestrial domains demonstrating the accuracy and generalisation properties of the algorithm for recognition and segmentation. Comparisons with conventional density estimation techniques are provided to illustrate the benefits of the new approach