67 research outputs found
Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks
Place recognition is an essential component of Simultaneous Localization And
Mapping (SLAM). Under severe appearance change, reliable place recognition is a
difficult perception task since the same place is perceptually very different
in the morning, at night, or over different seasons. This work addresses place
recognition as a domain translation task. Using a pair of coupled Generative
Adversarial Networks (GANs), we show that it is possible to generate the
appearance of one domain (such as summer) from another (such as winter) without
requiring image-to-image correspondences across the domains. Mapping between
domains is learned from sets of images in each domain without knowing the
instance-to-instance correspondence by enforcing a cyclic consistency
constraint. In the process, meaningful feature spaces are learned for each
domain, the distances in which can be used for the task of place recognition.
Experiments show that learned features correspond to visual similarity and can
be effectively used for place recognition across seasons.Comment: Accepted for publication in IEEE International Conference on Robotics
and Automation (ICRA), 201
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Analysis of Machine Learning Based Imputation of Missing Data
Data analysis and classification can be affected by the availability of missing data
in datasets. To deal with missing data, either deletion-based or imputation-based
methods are used that results in the reduction of data records or wrong predicted
value imputed by means/median respectively. A significant improvement can be
done if missing values are imputed more accurately with less computation cost.
In this work, a flow for analysis of machine learning-based algorithms for
missing data imputation is proposed. The K-nearest neighbors (KNN) and
Sequential KNN (SKNN) algorithms are used to impute missing values in
datasets using machine learning. Missing values handled using statistical
deletion approach (List-wise Deletion) and ML-based imputation methods (KNN
and SKNN) is then tested and compared using different ML classifiers (Support
Vector Machine and Decision Tree) to evaluate effectiveness of imputed data.
The used algorithms are compared in terms of accuracy, and results yielded that
the ML-based imputation method (SKNN) outperforms LD-based approach and
KNN method in terms of effectiveness of handling missing data in almost every dataset with both classification algorithms (SVM and DT)
High Frequency, High Accuracy Pointing onboard Nanosats using Neuromorphic Event Sensing and Piezoelectric Actuation
As satellites become smaller, the ability to maintain stable pointing
decreases as external forces acting on the satellite come into play. At the
same time, reaction wheels used in the attitude determination and control
system (ADCS) introduce high frequency jitter which can disrupt pointing
stability. For space domain awareness (SDA) tasks that track objects tens of
thousands of kilometres away, the pointing accuracy offered by current
nanosats, typically in the range of 10 to 100 arcseconds, is not sufficient. In
this work, we develop a novel payload that utilises a neuromorphic event sensor
(for high frequency and highly accurate relative attitude estimation) paired in
a closed loop with a piezoelectric stage (for active attitude corrections) to
provide highly stable sensor-specific pointing. Event sensors are especially
suited for space applications due to their desirable characteristics of low
power consumption, asynchronous operation, and high dynamic range. We use the
event sensor to first estimate a reference background star field from which
instantaneous relative attitude is estimated at high frequency. The
piezoelectric stage works in a closed control loop with the event sensor to
perform attitude corrections based on the discrepancy between the current and
desired attitude. Results in a controlled setting show that we can achieve a
pointing accuracy in the range of 1-5 arcseconds using our novel payload at an
operating frequency of up to 50Hz using a prototype built from
commercial-off-the-shelf components. Further details can be found at
https://ylatif.github.io/ultrafinestabilisatio
Prediction of Relative Humidity in a High Elevated Basin of Western Karakoram by Using Different Machine Learning Models
Accurate and reliable prediction of relative humidity is of great importance in all fields concerning global climate change. The current study has employed Multivariate Adaptive Regression Spline (MARS) and M5 Tree (M5T) models to predict the relative humidity in the Hunza River basin, Pakistan. Both the models provided the best prediction for the input scenario S6 (RHt-1, RHt-2, RHt-3, Tt-1, Tt-2, Tt-3). The statistical analysis displayed that the MARS model provided a better prediction of relative humidity as compared to M5T at all meteorological stations, especially, at Ziarat followed by Khunjerab and Naltar. The values of root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) were (5.98%, 5.43%, and 0.808) for Khunjerab; (6.58%, 5.08%, and 0.806) for Naltar; and (5.86%, 4.97%, 0.815) for Ziarat during the testing of MARS model whereas, the values were (6.14%, 5.56%, and 0.772) for Khunjerab; (6.19%, 5.58% and 0.762) for Naltar and (6.08%, 5.46%, 0.783) for Ziarat during the testing of M5T model. Both the models performed slightly better in training as compared to the testing stage. The current study encourages future research to be conducted at high altitude basins for the prediction of other meteorological variables using machine learning tools
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age
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