Learning and then recognizing a route, whether travelled during the day or at night, in clear inclement weather, summer winter is challenging task for state of art algorithms computer vision robotics. In this paper, we present new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead calculating single location most likely given current image, our calculates best candidate matching within every local sequence. Localization achieved by coherent sequences these "local...

The work presents a new approach to the problem of simultaneous localization and mapping - SLAM inspired by computational models hippocampus rodents. rodent has been extensively studied with respect navigation tasks, displays many properties desirable solution. RatSLAM is an implementation hippocampal model that can perform in real time on robot. It uses competitive attractor network integrate odometric information landmark sensing form consistent representation environment. Experimental...

This paper describes a biologically inspired approach to vision-only simultaneous localization and mapping (SLAM) on ground-based platforms. The core SLAM system, dubbed RatSLAM, is based computational models of the rodent hippocampus, coupled with lightweight vision system that provides odometry appearance information. RatSLAM builds map in an online manner, driving loop closure relocalization through sequences familiar visual scenes. Visual ambiguity managed by maintaining multiple...

The challenge of persistent navigation and mapping is to develop an autonomous robot system that can simultaneously localize, map navigate over the lifetime with little or no human intervention. Most solutions simultaneous localization (SLAM) problem aim produce highly accurate maps areas are assumed be static. In contrast, for must reliable goal-directed outcomes in environment constant flux. We investigate context performs mock deliveries a working office two-week period. solution was...

Appearance-based mapping and localisation is especially challenging when separate processes of occur at different times day. The problem exacerbated in the outdoors where continuous change sun angle can drastically affect appearance a scene. We confront this challenge by fusing probabilistic local feature based data association method FAB-MAP with pose cell filtering experience RatSLAM. evaluate effectiveness our amalgamation methods using five datasets captured throughout day from single...

Appearance-based loop closure techniques, which leverage the high information content of visual images and can be used independently pose, are now widely in robotic applications. The current state-of-the-art field is Fast Appearance-Based Mapping (FAB-MAP) having been demonstrated several seminal mapping experiments. In this paper, we describe OpenFABMAP, a fully open source implementation original FAB-MAP algorithm. Beyond benefits full user access to code, OpenFABMAP provides number...

In this paper we focus on the challenging problem of place categorization and semantic mapping a robot without environment-specific training. Motivated by their ongoing success in various visual recognition tasks, build our system upon state-of-the-art convolutional network. We overcome its closed-set limitations complementing network with series one-vs-all classifiers that can learn to recognize new classes online. Prior domain knowledge is incorporated embedding classification into...

This paper describes a vision-based obstacle detection and navigation system for use as part of robotic solution the sustainable intensification broad-acre agriculture. To be cost-effective, robotics must competitive with current human-driven farm machinery. Significant costs are in high-end localization sensors. Our demonstrates combination an inexpensive global positioning inertial vision single stereo detection. The design robot, including detailed descriptions three key parts system:...

This paper describes a new system, dubbed Continuous Appearance-based Trajectory Simultaneous Localisation and Mapping (CAT-SLAM), which augments sequential appearance-based place recognition with local metric pose filtering to improve the frequency reliability of loop closure. As in other approaches mapping, closure is performed without calculating global feature geometry or performing 3D map construction. Loop-closure uses probabilistic distribution possible closures along robot’s previous...

Modern SLAM systems with a depth sensor are able to reliably reconstruct dense 3D geometric maps of indoor scenes. Representing these in terms meaningful entities is step towards building semantic for autonomous robots. One approach segment the into objects using Conditional Random Fields (CRF), which requires large ground truth datasets train classification model. Additionally, CRF inference often computationally expensive. In this paper, we present an unsupervised geometric-based...

Actuators with deliberately added compliant elements in the transmission system are often described as improving safety of actuator at detriment performance. We show that our variant series elastic topology, velocity sourced actuator, has well defined performance characteristics make for improvements and over conventional high impedance actuators. The improvement was principally achieved by having tight control DC motor acts mechanical power source actuator. Results given point to transition...

To successfully navigate their habitats, many mammals use a combination of two mechanisms, path integration and calibration using landmarks, which together enable them to estimate location orientation, or pose. In large natural environments, both these mechanisms are characterized by uncertainty: the process is subject accumulation error, while landmark limited perceptual ambiguity. It remains unclear how animals form coherent spatial representations in presence such uncertainty. Navigation...

Spatial navigation requires the processing of complex, disparate and often ambiguous sensory data. The neurocomputations underpinning this vital ability remain poorly understood. Controversy remains as to whether multimodal information must be combined into a unified representation, consistent with Tolman's "cognitive map", or differential activation independent modules suffice explain observed behaviour. Here we demonstrate that key neural correlates spatial in darkness cannot explained if...

Visual localization in outdoor environments is often hampered by the natural variation appearance caused such things as weather phenomena, diurnal fluctuations lighting, and seasonal changes. Such changes are global across an environment and, case of light variation, change occurs a regular, cyclic manner. could be greatly improved if it were possible to predict particular location at time, based on past knowledge nature over time. In this paper, we investigate whether can learned...

The paper discusses robot navigation from biological inspiration. authors sought to build a model of the rodent brain that is suitable for practical navigation. core model, dubbed RatSLAM, has been demonstrated have exactly same advantages described earlier: it can build, maintain, and use maps simultaneously over extended periods time construct large complex areas very weak geometric information. work contrasts with other efforts embody models rat brains in robots. article describes key...

Farmers are under growing pressure to intensify production feed a population while managing environmental impact. Robotics has the potential address these challenges by replacing large complex farm machinery with fleets of small autonomous robots. This article presents our research toward goal developing teams robots that perform typical coverage operations. Making fleet economical requires use inexpensive sensors, such as cameras for localization and obstacle avoidance. To this end, we...

Simultaneous localization and mapping (SLAM) is one of the major challenges in mobile robotics. Probabilistic techniques using high-end range finding devices are well established field, but work has investigated vision-only approaches. This paper presents a method for generating approximate rotational translation velocity information from single vehicle-mounted consumer camera, without computationally expensive process tracking landmarks. The tested by employing it to provide odometric...

Micro aerial vehicles (MAVs) are a rapidly growing area of research and development in robotics. For autonomous robot operations, localization has typically been calculated using GPS, external camera arrays, or onboard range vision sensing. In cluttered indoor outdoor environments, sensing is the only viable option. this paper we present an appearance-based approach to visual SLAM on flying MAV low quality vision. Our consists place recognition algorithm that operates 1000 pixel images,...

In this paper we present for the first time a complete symbolic navigation system that performs goal-directed exploration to unfamiliar environments on physical robot. We introduce novel construct called abstract map link provided spatial information with observed and actual places in real world. Symbolic is using text recognition has been developed specifically application of reading door labels. study described paper, robot was floor plan destination. The destination specified by room...

Details the developments to date of an unmanned air vehicle (UAV) based on a standard size 60 model helicopter. The design goal is have helicopter achieve stable hover with aid INS and stereo vision. focus paper development artificial neural network (ANN) that makes use only data generate commands, which are used directly manipulate flight servos. Current results show networks incorporating some form recurrency (state history) offer little advantage over those without. At this stage, ANN has...