While many statistical consensus methods now exist, relatively little comparative benchmarking and integration of techniques has made it increasingly difficult to determine the current state-of-the-art, to evaluate the relative benefit of new methods, to understand where specific problems merit greater attention, and to measure field progress over time. To make such comparative evaluation easier for everyone, we present SQUARE, an open source shared task framework including benchmark datasets, defined tasks, standard metrics, and reference implementations with empirical results for several popular methods. In addition to measuring performance on a variety of public, real crowd datasets, the benchmark also varies supervision and noise by manipulating training size and labeling error. We envision SQUARE as dynamic and continually evolving, with new datasets and reference implementations being added according to community needs and interest. We invite community contributions and participation.
Abstract-LIDAR-only and camera-only approaches to global localization in planetary environments have relied heavily on availability of elevation data. The low-resolution nature of available DEMs limits the accuracy of these methods. Availability of new high-resolution planetary imagery motivates the rover localization method presented here. The method correlates terrain appearance with orthographic imagery. A rover generates a colorized 3D model of the local terrain using a panorama of camera and LIDAR data. This model is orthographically projected onto the ground plane to create a template image. The template is then correlated with available satellite imagery to determine rover location. No prior elevation data is necessary. Experiments in simulation demonstrate 2m accuracy. This method is robust to 30° differences in lighting angle between satellite and rover imagery. I. INTRODUCTIONGlobal localization for planetary rovers has bearing on many applications of exploratory and scientific interest. Rovers will be expected to maintain precise pose estimates over long traverses, build detailed models of interesting regions, and accurately correlate in-situ measurements with satellite imagery to achieve high science return. Autonomous global localization results in more efficient use of human operators, enabling them to focus on scientific and exploration objectives instead of rover positioning. It also makes possible driving without communication for operations such as forays into radio-dark craters at the poles of the Moon. Despite its impact, precise, autonomous, global localization has not yet been achieved. Current means for localization include absolute methods like radio tracking from a satellite and matching to elevation data, and relative methods like wheel odometry, inertial navigation, and visual odometry. Absolute methods using radio rely on infrastructure and are limited to operate within line-of-sight of the radio beacon. Elevation data is often much lower resolution than available imagery, limiting localization precision. Relative methods drift over time and become inaccurate without human input. These approaches are insufficient for long-range traverse where regular updates from Earth are infeasible and line of sight is not
This paper presents complementary flyover and surface exploration for reconnaissance of planetary point destinations, like skylights and polar crater rims, where local 3D detail matters. Recent breakthroughs in precise, safe landing enable spacecraft to touch down within a few hundred meters of target destinations. These precision trajectories provide unprecedented access to bird's-eye views of the target site and enable a paradigm shift in terrain modeling and path planning. High-angle flyover views penetrate deep into concave features while low-angle rover perspectives provide detailed views of areas that cannot be seen in flight. By combining flyover and rover sensing in a complementary manner, coverage is improved and rover trajectory length is reduced by 40%. Simulation results for modeling a Lunar skylight are presented.
We introduce a variational quantum algorithm to solve unconstrained black box binary optimization problems, i.e., problems in which the objective function is given as black box. This is in contrast to the typical setting of quantum algorithms for optimization where a classical objective function is provided as a given Quadratic Unconstrained Binary Optimization problem and mapped to a sum of Pauli operators. Furthermore, we provide theoretical justification for our method based on convergence guarantees of quantum imaginary time evolution.To investigate the performance of our algorithm and its potential advantages, we tackle a challenging real-world optimization problem: feature selection. This refers to the problem of selecting a subset of relevant features to use for constructing a predictive model such as fraud detection. Optimal feature selection---when formulated in terms of a generic loss function---offers little structure on which to build classical heuristics, thus resulting primarily in ‘greedy methods’. This leaves room for (near-term) quantum algorithms to be competitive to classical state-of-the-art approaches. We apply our quantum-optimization-based feature selection algorithm, termed VarQFS, to build a predictive model for a credit risk data set with 20 and 59 input features (qubits) and train the model using quantum hardware and tensor-network-based numerical simulations, respectively. We show that the quantum method produces competitive and in certain aspects even better performance compared to traditional feature selection techniques used in today's industry.
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