How was your day? Online visual workspace summaries using incremental clustering in topic space

Someday mobile robots will operate continually. Day after day, they will be in receipt of a never ending stream of images. In anticipation of this, this paper is about having a mobile robot generate apt and compact summaries of its life experience. We consider a robot moving around its environment both revisiting and exploring, accruing images as it goes. We describe how we can choose a subset of images to summarise the robot's cumulative visual experience. Moreover we show how to do this such that the time cost of generating an summary is largely independent of the total number of images processed. No one day is harder to summarise than any other.Micro Autonomous Consortium Systems and Technology (United States. Army Research Laboratory (Grant W911NF-08-2-0004))United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N00014-09-1-1051)United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N00014-09-1-1031

Scene signatures: localised and point-less features for localisation

This paper is about localising across extreme lighting and weather conditions. We depart from the traditional point-feature-based approach as matching under dramatic appearance changes is a brittle and hard thing. Point feature detectors are fixed and rigid procedures which pass over an image examining small, low-level structure such as corners or blobs. They apply the same criteria applied all images of all places. This paper takes a contrary view and asks what is possible if instead we learn a bespoke detector for every place. Our localisation task then turns into curating a large bank of spatially indexed detectors and we show that this yields vastly superior performance in terms of robustness in exchange for a reduced but tolerable metric precision. We present an unsupervised system that produces broad-region detectors for distinctive visual elements, called scene signatures, which can be associated across almost all appearance changes. We show, using 21km of data collected over a period of 3 months, that our system is capable of producing metric localisation estimates from night-to-day or summer-to-winter conditions

Machine Vision for intelligent Semi-Autonomous Transport (MV-iSAT)

AbstractThe primary focus was to develop a vision-based system suitable for the navigation and mapping of an indoor, single-floor environment. Devices incorporating an iSAT system could be used as ‘self-propelled’ shopping carts in high-end retail stores or as automated luggage routing systems in airports. The primary design feature of this system is its Field Programmable Gate Array (FPGA) core, chosen for its strengths in parallelism and pipelining. Image processing has been successfully demonstrated in real-time using FPGA hardware. Remote feedback and monitoring was broadcasted to a host computer via a local area network. Deadlines as short as 40ns have been met by a custom built memory-based arbitration scheme. It is hoped that the iSAT platform will provide the basis for future work on advanced FPGA-based machine-vision algorithms for mobile robotics

View management for lifelong visual maps

The time complexity of making observations and loop closures in a graph-based visual SLAM system is a function of the number of views stored. Clever algorithms, such as approximate nearest neighbor search, can make this function sub-linear. Despite this, over time the number of views can still grow to a point at which the speed and/or accuracy of the system becomes unacceptable, especially in computation- and memory-constrained SLAM systems. However, not all views are created equal. Some views are rarely observed, because they have been created in an unusual lighting condition, or from low quality images, or in a location whose appearance has changed. These views can be removed to improve the overall performance of a SLAM system. In this paper, we propose a method for pruning views in a visual SLAM system to maintain its speed and accuracy for long term use.Comment: IEEE International Conference on Intelligent Robots and Systems (IROS), 201

Implementing Simultaneous Localization and Mapping

This paper will provide an overview of existing SLAM techniques and a brief review of several implementations. This will be done by describing the SLAM problem formulation, followed by the details involved in implementing both the image recognition and motion estimation approaches. The application of SLAM using other sensors, such as radar, will also be discussed

Information-based view initialization in visual SLAM with a single omnidirectional camera

© 2015 Elsevier B.V. All rights reserved. This paper presents a novel mechanism to initiate new views within the map building process for an EKF-based visual SLAM (Simultaneous Localization and Mapping) approach using omnidirectional images. In presence of non-linearities, the EKF is very likely to compromise the final estimation. Particularly, the omnidirectional observation model induces non-linear errors, thus it becomes a potential source of uncertainty. To deal with this issue we propose a novel mechanism for view initialization which accounts for information gain and losses more efficiently. The main outcome of this contribution is the reduction of the map uncertainty and thus the higher consistency of the final estimation. Its basis relies on a Gaussian Process to infer an information distribution model from sensor data. This model represents feature points existence probabilities and their information content analysis leads to the proposed view initialization scheme. To demonstrate the suitability and effectiveness of the approach we present a series of real data experiments conducted with a robot equipped with a camera sensor and map model solely based on omnidirectional views. The results reveal a beneficial reduction on the uncertainty but also on the error in the pose and the map estimate