How to Train a CAT: Learning Canonical Appearance Transformations for Direct Visual Localization Under Illumination Change

Direct visual localization has recently enjoyed a resurgence in popularity with the increasing availability of cheap mobile computing power. The competitive accuracy and robustness of these algorithms compared to state-of-the-art feature-based methods, as well as their natural ability to yield dense maps, makes them an appealing choice for a variety of mobile robotics applications. However, direct methods remain brittle in the face of appearance change due to their underlying assumption of photometric consistency, which is commonly violated in practice. In this paper, we propose to mitigate this problem by training deep convolutional encoder-decoder models to transform images of a scene such that they correspond to a previously-seen canonical appearance. We validate our method in multiple environments and illumination conditions using high-fidelity synthetic RGB-D datasets, and integrate the trained models into a direct visual localization pipeline, yielding improvements in visual odometry (VO) accuracy through time-varying illumination conditions, as well as improved metric relocalization performance under illumination change, where conventional methods normally fail. We further provide a preliminary investigation of transfer learning from synthetic to real environments in a localization context. An open-source implementation of our method using PyTorch is available at https://github.com/utiasSTARS/cat-net.Comment: In IEEE Robotics and Automation Letters (RA-L) and presented at the IEEE International Conference on Robotics and Automation (ICRA'18), Brisbane, Australia, May 21-25, 201

Learning Matchable Image Transformations for Long-term Metric Visual Localization

Long-term metric self-localization is an essential capability of autonomous mobile robots, but remains challenging for vision-based systems due to appearance changes caused by lighting, weather, or seasonal variations. While experience-based mapping has proven to be an effective technique for bridging the `appearance gap,' the number of experiences required for reliable metric localization over days or months can be very large, and methods for reducing the necessary number of experiences are needed for this approach to scale. Taking inspiration from color constancy theory, we learn a nonlinear RGB-to-grayscale mapping that explicitly maximizes the number of inlier feature matches for images captured under different lighting and weather conditions, and use it as a pre-processing step in a conventional single-experience localization pipeline to improve its robustness to appearance change. We train this mapping by approximating the target non-differentiable localization pipeline with a deep neural network, and find that incorporating a learned low-dimensional context feature can further improve cross-appearance feature matching. Using synthetic and real-world datasets, we demonstrate substantial improvements in localization performance across day-night cycles, enabling continuous metric localization over a 30-hour period using a single mapping experience, and allowing experience-based localization to scale to long deployments with dramatically reduced data requirements.Comment: In IEEE Robotics and Automation Letters (RA-L) and presented at the IEEE International Conference on Robotics and Automation (ICRA'20), Paris, France, May 31-June 4, 202

Hyperbanana Graphs

A bar-and-joint framework is a finite set of points together with specified distances between selected pairs. In rigidity theory we seek to understand when the remaining pairwise distances are also fixed. If there exists a pair of points which move relative to one another while maintaining the given distance constraints, the framework is flexible; otherwise, it is rigid. Counting conditions due to Maxwell give a necessary combinatorial criterion for generic minimal bar-and-joint rigidity in all dimensions. Laman showed that these conditions are also sufficient for frameworks in R^2. However, the flexible "double banana" shows that Maxwell's conditions are not sufficient to guarantee rigidity in R^3. We present a generalization of the double banana to a family of hyperbananas. In dimensions 3 and higher, these are (infinitesimally) flexible, providing counterexamples to the natural generalization of Laman's theorem

Relationships among Prices across Alternative Marketing Arrangements for Fed Cattle and Hogs

Reduced reliance on cash market prices for fed cattle and hogs raise questions about the role of cash prices in price discovery. We use seven years of weekly data from mandatory price reports to determine whether or not cash market prices are cointegrated with other procurement prices and then test for causality among the price series. Cash prices were cointegrated with all but one procurement price series. Cash market prices Granger cause all other procurement prices. Bidirectional causality was found in some but not all cases. Thus, cash market prices remain of central importance in price discovery for fed cattle and hogs.cattle, cointegration, causality, hogs, Johansen, marketing, prices, Stock-Watson, vector error correction, Livestock Production/Industries, Marketing, Q13(of Q18),

Transport of c-MYC by Kinesin-1 for proteasomal degradation in the cytoplasm

Abstractc-MYC is an oncogenic transcription factor that is degraded by the proteasome pathway. However, the mechanism that regulates delivery of c-MYC to the proteasome for degradation is not well characterized. Here, the results show that the motor protein complex Kinesin-1 transports c-MYC to the cytoplasm for proteasomal degradation. Inhibition of Kinesin-1 function enhanced ubiquitination of c-MYC and induced aggregation of c-MYC in the cytoplasm. Transport studies showed that the c-MYC aggregates moved from the nucleus to the cytoplasm and KIF5B is responsible for the transport in the cytoplasm. Furthermore, inhibition of the proteasomal degradation process also resulted in an accumulation of c-MYC aggregates in the cytoplasm. Moreover, Kinesin-1 was shown to interact with c-MYC and the proteasome subunit S6a. Inhibition of Kinesin-1 function also reduced c-MYC-dependent transformation activities. Taken together, the results strongly suggest that Kinesin-1 transports c-MYC for proteasomal degradation in the cytoplasm and the proper degradation of c-MYC mediated by Kinesin-1 transport is important for transformation activities of c-MYC. In addition, the results indicate that Kinesin-1 transport mechanism is important for degradation of a number of other proteins as well