Delta Descriptors: Change-Based Place Representation for Robust Visual Localization

Visual place recognition is challenging because there are so many factors that can cause the appearance of a place to change, from day-night cycles to seasonal change to atmospheric conditions. In recent years a large range of approaches have been developed to address this challenge including deep-learnt image descriptors, domain translation, and sequential filtering, all with shortcomings including generality and velocity-sensitivity. In this paper we propose a novel descriptor derived from tracking changes in any learned global descriptor over time, dubbed Delta Descriptors. Delta Descriptors mitigate the offsets induced in the original descriptor matching space in an unsupervised manner by considering temporal differences across places observed along a route. Like all other approaches, Delta Descriptors have a shortcoming - volatility on a frame to frame basis - which can be overcome by combining them with sequential filtering methods. Using two benchmark datasets, we first demonstrate the high performance of Delta Descriptors in isolation, before showing new state-of-the-art performance when combined with sequence-based matching. We also present results demonstrating the approach working with four different underlying descriptor types, and two other beneficial properties of Delta Descriptors in comparison to existing techniques: their increased inherent robustness to variations in camera motion and a reduced rate of performance degradation as dimensional reduction is applied. Source code is made available at https://github.com/oravus/DeltaDescriptors.Comment: 8 pages and 7 figures. Published in 2020 IEEE Robotics and Automation Letters (RA-L

A UAV Based CMOS Ku-Band Metasurface FMCW Radar System for Low-Altitude Snowpack Sensing

This article presents development of a UAV based frequency modulated continuous wave (FMCW) radar system for remotely sensing the water contained within snowpacks. To make the radar system compatible with the payload requirements of small UAV platforms, the radar electronics are implemented with CMOS technology, and the antenna is implemented as an extremely compact and lightweight metasurface (MTS) antenna. This article will discuss how the high absorption losses of snowpacks lead to dynamic range requirements much stricter than FMCW radars used for automotive and other sensing applications, and how these requirements are met through antenna isolation, leakage calibration and exploitation of the range correlation effect. The article discusses in detail the implementation of the radar system, the CMOS microwave and digital circuitry, and the MTS antenna. The developed radar was mounted on a drone and conducted surveys in both Idaho and Alaska during the 2022-2023 winter season. We present several of those field results

Unsupervised visual perception-based representation learning for time-series and trajectories

Representing time-series without relying on the domain knowledge and independent of the end-task is a challenging problem. The same situation applies to trajectory data as well, where sufficient labelled information is often unavailable to learn effective representations. This thesis addresses this problem and explores unsupervised ways of representing the temporal data. The novel methods imitate the human visual perception of the pictorial depiction of such data based on deep learning

DeLTa : Deep local pattern representation for time-series clustering and classification using visual perception

Time-series analysis is of enormous significance to a multitude of domains such as Internet-of-Things (IoT), prognostics, health, and robotics. Machine learning tasks require time-series data in the form of features for the application of (un)supervised algorithms. The existing feature representation methods lack generality and are domain-specific, especially those based on supervised learning. In this paper, we propose a novel time-series feature representation method based on feature transformation and feature learning. The feature transformation process is inspired by the human cognitive thinking used in visual recognition, where the 1-D time-series data is transformed into a 2-D image dataset. A feature set is learned by imposing a pre-trained convolutional neural network on the transformed search space. This generates two complementary high-dimensional feature sets: (1) one with the matching of the overall 2-D layout of the time-series; and (2) another with matching based on the activation of the local 2-D patterns irrespective of the overall layout. Empirical analysis on a large number of benchmark datasets shows the advantage of the domain-agnostic nature of DeLTa in achieving higher accuracy in comparison to relevant benchmarking methods. Source code is publicly available at https://github.com/technophyte/DeLTa