Towards persistent visual navigation using SMART

In this paper, we present SMART (Sequence Matching Across Route Traversals): a vision- based place recognition system that uses whole image matching techniques and odometry information to improve the precision-recall performance, latency and general applicability of the SeqSLAM algorithm. We evaluate the system’s performance on challenging day and night journeys over several kilometres at widely varying vehicle velocities from 0 to 60 km/h, compare performance to the current state-of- the-art SeqSLAM algorithm, and provide parameter studies that evaluate the effectiveness of each system component. Using 30-metre sequences, SMART achieves place recognition performance of 81% recall at 100% precision, outperforming SeqSLAM, and is robust to significant degradations in odometry.

All-environment visual place recognition with SMART

This paper presents Sequence Matching Across Route Traversals (SMART); a generally applicable sequence-based place recognition algorithm. SMART provides invariance to changes in illumination and vehicle speed while also providing moderate pose invariance and robustness to environmental aliasing. We evaluate SMART on vehicles travelling at highly variable speeds in two challenging environments; firstly, on an all-terrain vehicle in an off-road, forest track and secondly, using a passenger car traversing an urban environment across day and night. We provide comparative results to the current state-of-the-art SeqSLAM algorithm and investigate the effects of altering SMART’s image matching parameters. Additionally, we conduct an extensive study of the relationship between image sequence length and SMART’s matching performance. Our results show viable place recognition performance in both environments with short 10-metre sequences, and up to 96% recall at 100% precision across extreme day-night cycles when longer image sequences are used.

Towards Vision-Based Pose- and Condition-Invariant Place Recognition along Routes

Vision-based place recognition involves recognising familiar places despite changes in environmental conditions or camera viewpoint (pose). Existing training-free methods exhibit excellent invariance to either of these challenges, but not both simultaneously. In this paper, we present a technique for condition-invariant place recognition across large lateral platform pose variance for vehicles or robots travelling along routes. Our approach combines sideways facing cameras with a new multi-scale image comparison technique that generates synthetic views for input into the condition-invariant Sequence Matching Across Route Traversals (SMART) algorithm. We evaluate the system’s performance on multi-lane roads in two different environments across day-night cycles. In the extreme case of day-night place recognition across the entire width of a four-lane-plus-median-strip highway, we demonstrate performance of up to 44% recall at 100% precision, where current state-of-the-art fails.

Automated sensory data alignment for environmental and epidermal change monitoring

In this paper we present research adapting a state of the art condition-invariant robotic place recognition algorithm to the role of automated inter- and intra-image alignment of sensor observations of environmental and skin change over time. The approach involves inverting the typical criteria placed upon navigation algorithms in robotics; we exploit rather than attempt to fix the limited camera viewpoint invariance of such algorithms, showing that approximate viewpoint repetition is realistic in a wide range of environments and medical applications. We demonstrate the algorithms automatically aligning challenging visual data from a range of real-world applications: ecological monitoring of environmental change, aerial observation of natural disasters including flooding, tsunamis and bushfires and tracking wound recovery and sun damage over time and present a prototype active guidance system for enforcing viewpoint repetition. We hope to provide an interesting case study for how traditional research criteria in robotics can be inverted to provide useful outcomes in applied situations.

Automatic image scaling for place recognition in changing environments

Robustness to variations in environmental conditions and camera viewpoint is essential for long-term place recognition, navigation and SLAM. Existing systems typically solve either of these problems, but invariance to both remains a challenge. This paper presents a training-free approach to lateral viewpoint- and condition-invariant, vision-based place recognition. Our successive frame patch-tracking technique infers average scene depth along traverses and automatically rescales views of the same place at different depths to increase their similarity. We combine our system with the condition-invariant SMART algorithm and demonstrate place recognition between day and night, across entire 4-lane-plus-median-strip roads, where current algorithms fail.

Place recognition with ConvNet landmarks: Viewpoint-robust, condition-robust, training-free

Place recognition has long been an incompletely solved problem in that all approaches involve significant compromises. Current methods address many but never all of the critical challenges of place recognition – viewpoint-invariance, condition-invariance and minimizing training requirements. Here we present an approach that adapts state-of-the-art object proposal techniques to identify potential landmarks within an image for place recognition. We use the astonishing power of convolutional neural network features to identify matching landmark proposals between images to perform place recognition over extreme appearance and viewpoint variations. Our system does not require any form of training, all components are generic enough to be used off-the-shelf. We present a range of challenging experiments in varied viewpoint and environmental conditions. We demonstrate superior performance to current state-of-the- art techniques. Furthermore, by building on existing and widely used recognition frameworks, this approach provides a highly compatible place recognition system with the potential for easy integration of other techniques such as object detection and semantic scene interpretation.

Repeatable Condition-Invariant Visual Odometry for Sequence-Based Place Recognition

This paper describes a vision-only system for place recognition in environments that are tra- versed at different times of day, when chang- ing conditions drastically affect visual appear- ance, and at different speeds, where places aren’t visited at a consistent linear rate. The ma- jor contribution is the removal of wheel-based odometry from the previously presented algo- rithm (SMART), allowing the technique to op- erate on any camera-based device; in our case a mobile phone. While we show that the di- rect application of visual odometry to our night- time datasets does not achieve a level of perfor- mance typically needed, the VO requirements of SMART are orthogonal to typical usage: firstly only the magnitude of the velocity is required, and secondly the calculated velocity signal only needs to be repeatable in any one part of the environment over day and night cycles, but not necessarily globally consistent. Our results show that the smoothing effect of motion constraints is highly beneficial for achieving a locally consis- tent, lighting-independent velocity estimate. We also show that the advantage of our patch-based technique used previously for frame recogni- tion, surprisingly, does not transfer to VO, where SIFT demonstrates equally good performance. Nevertheless, we present the SMART system us- ing only vision, which performs sequence-base place recognition in extreme low-light condi- tions where standard 6-DOF VO fails and that improves place recognition performance over odometry-less benchmarks, approaching that of wheel odometry.

Mathematics in the primary school : a sense of progression.

Proporciona un conjunto de principios básicos para la enseñanza de las matemáticas en la escuela primaria a través de los principales temas del programa de estudios. Los autores exploran la comprensión de los niños en áreas claves de las matemáticas en la etapa Fundación (Foundation Stage) y la etapas clave de las fases 1 y 2 (Key Stage 1 and Key Stage 2). Identifican importantes enfoques de la enseñanza, incluyendo el uso de calculadoras y ordenadores, y hacen énfasis en el cálculo mental y en la solución de problemas. Se utilizan estudios de casos para ilustrar como ponerlos en práctica y cómo los niños responden a ellos. Subraya la importancia del conocimiento matemático de los propios profesores y les ofrece orientación y consejos prácticos.

Docket and fees book / 1791-1797

Records of cases heard in the Massachusetts Court of Common Pleas (Middlesex Co.) in Cambridge, Mass., and the New Hampshire Inferior Court of Common Pleas (Hillsborough Co.) in Amherst, N.H and matters brought before justices of the peace. Records identify the litigants, with some notes on fees and settlements; many of the cases concern debts. Justices of the peace include: Israel Atherton (Lancaster, Mass.); Samuel Dana (Amherst, N.H.); Joshua Longley (Shirley, Mass.); Nathaniel Paine (Worcester, Mass.); James Prescott (Westford, Mass.); Jeremiah Stiles (Keene, N.H.); William Swan (Groton, Mass.); Sampson Tuttle (Littleton, Mass.); and Henry Woods (Pepperell, Mass.).

Commonplace book of John Bullard, 1775-1779

This bound volume contains excerpts copied by Jonathan Bullard from books he read as a student at Harvard in the mid 1770s. Excerpts include an unattributed poem titled "On Friendship," which appeared in the "poetical essays" section of Volume 36 of the London Magazine in 1767; Joseph Butler, The Analogy of Religion, 1736; The Quaker's Grace; a history of England; Newton's laws; Plutarch's Morals; Benjamin Franklin's writings on the Aurora Borealis. The volume also includes several extracts from articles about the death of John Paddock (Class of 1776), who drowned in the Charles in the summer of 1773, sheet music for two songs, "The Rapture," and "A Song" from Henry Harington's "Damon and Chlora," and a transcription of the satirical "Book of Harvard," written in response to the Butter Rebellion of 1766. Interleaved in the middle of the volume is a transcription from an ecclesiastical event moderated by Ebenezer Bridge in Medford, Mass. on November 20, 1779. The variety of texts suggests the commonplace book was not used solely for academic works.

Middlesex County, Massachusetts, 1856 (Raster Image)

This layer is a georeferenced raster image of the historic paper map entitled: Map of Middlesex County, Massachusetts, the details from original surveys under the direction of Henry F. Walling, supt. of the state map ; Thos. W. Baker, draughtsman. It was published by Smith & Bumstead in 1856. Scale 1:50,000. The image inside the map neatline is georeferenced to the surface of the earth and fit to the Massachusetts State Plane Coordinate System, Mainland Zone (in Feet) (Fipszone 2001). All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, or other information associated with the principal map. This map shows features such as roads, railroads, drainage, public buildings, schools, churches, cemeteries, industry locations (e.g. mills, factories, mines, etc.), private buildings with names of property owners, town and county boundaries and more. Covers also parts of Boston. Relief is shown by hachures. It includes many cadastral insets of individual county towns and villages, and an inset geological map of county. It also includes illustrations, business directories, and tables of statistics and distances. This layer is part of a selection of digitally scanned and georeferenced historic maps of Massachusetts from the Harvard Map Collection. These maps typically portray both natural and manmade features. The selection represents a range of regions, originators, ground condition dates (1755-1922), scales, and purposes. The digitized selection includes maps of: the state, Massachusetts counties, town surveys, coastal features, real property, parks, cemeteries, railroads, roads, public works projects, etc.