Using deep autoencoders to investigate image matching in visual navigation

This paper discusses the use of deep autoencoder networks to find a compressed representation of an image, which can be used for visual naviga-tion. Images reconstructed from the compressed representation are tested to see if they retain enough information to be used as a visual compass (in which an image is matched with another to recall a bearing/movement direction) as this ability is at the heart of a visual route navigation algorithm. We show that both reconstructed images and compressed representations from different layers of the autoencoder can be used in this way, suggesting that a compact image code is sufficient for visual navigation and that deep networks hold promise for find-ing optimal visual encodings for this task

Landmarks or panoramas: what do navigating ants attend to for guidance?

<p>Abstract</p> <p>Background</p> <p>Insects are known to rely on terrestrial landmarks for navigation. Landmarks are used to chart a route or pinpoint a goal. The distant panorama, however, is often thought not to guide navigation directly during a familiar journey, but to act as a contextual cue that primes the correct memory of the landmarks.</p> <p>Results</p> <p>We provided <it>Melophorus bagoti </it>ants with a huge artificial landmark located right near the nest entrance to find out whether navigating ants focus on such a prominent visual landmark for homing guidance. When the landmark was displaced by small or large distances, ant routes were affected differently. Certain behaviours appeared inconsistent with the hypothesis that guidance was based on the landmark only. Instead, comparisons of panoramic images recorded on the field, encompassing both landmark and distal panorama, could explain most aspects of the ant behaviours.</p> <p>Conclusion</p> <p>Ants navigating along a familiar route do not focus on obvious landmarks or filter out distal panoramic cues, but appear to be guided by cues covering a large area of their panoramic visual field, including both landmarks and distal panorama. Using panoramic views seems an appropriate strategy to cope with the complexity of natural scenes and the poor resolution of insects' eyes. The ability to isolate landmarks from the rest of a scene may be beyond the capacity of animals that do not possess a dedicated object-perception visual stream like primates.</p

Ant homing ability is not diminished when traveling backwards

Ants are known to be capable of homing to their nest after displacement to a novel location. This is widely assumed to involve some form of retinotopic matching between their current view and previously experienced views. One simple algorithm proposed to explain this behavior is continuous retinotopic alignment, in which the ant constantly adjusts its heading by rotating to minimize the pixel-wise difference of its current view from all views stored while facing the nest. However, ants with large prey items will often drag them home while facing backwards. We tested whether displaced ants (Myrmecia croslandi) dragging prey could still home despite experiencing an inverted view of their surroundings under these conditions. Ants moving backwards with food took similarly direct paths to the nest as ants moving forward without food, demonstrating that continuous retinotopic alignment is not a critical component of homing. It is possible that ants use initial or intermittent retinotopic alignment, coupled with some other direction stabilizing cue that they can utilize when moving backward. However, though most ants dragging prey would occasionally look toward the nest, we observed that their heading direction was not noticeably improved afterwards. We assume ants must use comparison of current and stored images for corrections of their path, but suggest they are either able to chose the appropriate visual memory for comparison using an additional mechanism; or can make such comparisons without retinotopic alignment

Insect-inspired visual navigation for flying robots

This paper discusses the implementation of insect-inspired visual navigation strategies in flying robots, in particular focusing on the impact of changing height. We start by assessing the information available at different heights for visual homing in natural environments, comparing results from an open environment against one where trees and bushes are closer to the camera. We then test a route following algorithm using a gantry robot and show that a robot would be able to successfully navigate a route at a variety of heights using images saved at a different height

Visual navigation in ants

Les remarquables capacités de navigation des insectes nous prouvent à quel point ces " mini-cerveaux " peuvent produire des comportements admirablement robustes et efficaces dans des environnements complexes. En effet, être capable de naviguer de façon efficace et autonome dans un environnement parfois hostile (désert, forêt tropicale) sollicite l'intervention de nombreux processus cognitifs impliquant l'extraction, la mémorisation et le traitement de l'information spatiale préalables à une prise de décision locomotrice orientée dans l'espace. Lors de leurs excursions hors du nid, les insectes tels que les abeilles, guêpes ou fourmis, se fient à un processus d'intégration du trajet, mais également à des indices visuels qui leur permettent de mémoriser des routes et de retrouver certains sites alimentaires familiers et leur nid. L'étude des mécanismes d'intégration du trajet a fait l'objet de nombreux travaux, par contre, nos connaissances à propos de l'utilisation d'indices visuels sont beaucoup plus limitées et proviennent principalement d'études menées dans des environnements artificiellement simplifiés, dont les conclusions sont parfois difficilement transposables aux conditions naturelles. Cette thèse propose une approche intégrative, combinant 1- des études de terrains et de laboratoire conduites sur deux espèces de fourmis spécialistes de la navigation visuelle (Melophorus bagoti et Gigantiops destructor) et 2- des analyses de photos panoramiques prisent aux endroits où les fourmis naviguent qui permettent de quantifier objectivement l'information visuelle accessible à l'insecte. Les résultats convergents obtenus sur le terrain et au laboratoire permettent de montrer que, chez ces deux espèces, les fourmis ne fragmentent pas leur monde visuel en multiples objets indépendants, et donc ne mémorisent pas de 'repères visuels' ou de balises particuliers comme le ferait un être humain. En fait, l'efficacité de leur navigation émergerait de l'utilisation de paramètres visuels étendus sur l'ensemble de leur champ visuel panoramique, incluant repères proximaux comme distaux, sans les individualiser. Contre-intuitivement, de telles images panoramiques, même à basse résolution, fournissent une information spatiale précise et non ambiguë dans les environnements naturels. Plutôt qu'une focalisation sur des repères isolés, l'utilisation de vues dans leur globalité semble être plus efficace pour représenter la complexité des scènes naturelles et être mieux adaptée à la basse résolution du système visuel des insectes. Les photos panoramiques enregistrées peuvent également servir à l'élaboration de modèles navigationnels. Les prédictions de ces modèles sont ici directement comparées au comportement des fourmis, permettant ainsi de tester et d'améliorer les différentes hypothèses envisagées. Cette approche m'a conduit à la conclusion selon laquelle les fourmis utilisent leurs vues panoramiques de façons différentes suivant qu'elles se déplacent en terrain familier ou non. Par exemple, aligner son corps de manière à ce que la vue perçue reproduise au mieux l'information mémorisée est une stratégie très efficace pour naviguer le long d'une route bien connue ; mais n'est d'aucune efficacité si l'insecte se retrouve en territoire nouveau, écarté du chemin familier. Dans ces cas critiques, les fourmis semblent recourir à une seconde stratégie qui consiste à se déplacer vers les régions présentant une ligne d'horizon plus basse que celle mémorisée, ce qui généralement conduit vers le terrain familier. Afin de choisir parmi ces deux différentes stratégies, les fourmis semblent tout simplement se fier au degré de familiarisation avec le panorama perçu. Cette thèse soulève aussi la question de la nature de l'information visuelle mémorisée par les insectes. Le modèle du " snapshot " qui prédomine dans la littérature suppose que les fourmis mémorisent une séquence d'instantanés photographiques placés à différents points le long de leurs routes. A l'inverse, les résultats obtenus dans le présent travail montrent que l'information visuelle mémorisée au bout d'une route (15 mètres) modifie l'information mémorisée à l'autre extrémité de cette même route, ce qui suggère que la connaissance visuelle de l'ensemble de la route soit compactée en une seule et même représentation mémorisée. Cette hypothèse s'accorde aussi avec d'autres de nos résultats montrant que la mémoire visuelle ne s'acquiert pas instantanément, mais se développe et s'affine avec l'expérience répétée. Lorsqu'une fourmi navigue le long de sa route, ses récepteurs visuels sont stimulés de façon continue par une scène évoluant doucement et régulièrement au fur et à mesure du déplacement. Mémoriser un pattern général de stimulations, plutôt qu'une série de " snapshots " indépendants et très ressemblants les uns aux autres, constitue une hypothèse parcimonieuse. Cette hypothèse s'applique en outre particulièrement bien aux modèles en réseaux de neurones, suggérant sa pertinence biologique. Dans l'ensemble, cette thèse s'intéresse à la nature des perceptions et de la mémoire visuelle des fourmis, ainsi qu'à la manière dont elles sont intégrées et traitées afin de produire une réponse navigationnelle appropriée. Nos résultats sont aussi discutés dans le cadre de la cognition comparée. Insectes comme vertébrés ont résolu le même problème qui consiste à naviguer de façon efficace sur terre. A la lumière de la théorie de l'évolution de Darwin, il n'y a 'a priori' aucune raison de penser qu'il existe une forme de transition brutale entre les mécanismes cognitifs des différentes espèces animales. Le fossé marqué entre insectes et vertébrés au sein des sciences cognitives pourrait bien être dû à des approches différentes plutôt qu'à de vraies différences ontologiques. Historiquement, l'étude de la navigation de l'insecte a suivi une approche de type 'bottom-up' qui recherche comment des comportements apparemment complexes peuvent découler de mécanismes simples. Ces solutions parcimonieuses, comme celles explorées dans cette thèse, peuvent fournir de remarquables hypothèses de base pour expliquer la navigation chez d'autres espèces animales aux cerveaux et comportements apparemment plus complexes, contribuant ainsi à une véritable cognition comparée.Navigating efficiently in the outside world requires many cognitive abilities like extracting, memorising, and processing information. The remarkable navigational abilities of insects are an existence proof of how small brains can produce exquisitely efficient, robust behaviour in complex environments. During their foraging trips, insects, like ants or bees, are known to rely on both path integration and learnt visual cues to recapitulate a route or reach familiar places like the nest. The strategy of path integration is well understood, but much less is known about how insects acquire and use visual information. Field studies give good descriptions of visually guided routes, but our understanding of the underlying mechanisms comes mainly from simplified laboratory conditions using artificial, geometrically simple landmarks. My thesis proposes an integrative approach that combines 1- field and lab experiments on two visually guided ant species (Melophorus bagoti and Gigantiops destructor) and 2- an analysis of panoramic pictures recorded along the animal's route. The use of panoramic pictures allows an objective quantification of the visual information available to the animal. Results from both species, in the lab and the field, converged, showing that ants do not segregate their visual world into objects, such as landmarks or discrete features, as a human observers might assume. Instead, efficient navigation seems to arise from the use of cues widespread on the ants' panoramic visual field, encompassing both proximal and distal objects together. Such relatively unprocessed panoramic views, even at low resolution, provide remarkably unambiguous spatial information in natural environment. Using such a simple but efficient panoramic visual input, rather than focusing on isolated landmarks, seems an appropriate strategy to cope with the complexity of natural scenes and the poor resolution of insects' eyes. Also, panoramic pictures can serve as a basis for running analytical models of navigation. The predictions of these models can be directly compared with the actual behaviour of real ants, allowing the iterative tuning and testing of different hypotheses. This integrative approach led me to the conclusion that ants do not rely on a single navigational technique, but might switch between strategies according to whether they are on or off their familiar terrain. For example, ants can recapitulate robustly a familiar route by simply aligning their body in a way that the current view matches best their memory. However, this strategy becomes ineffective when displaced away from the familiar route. In such a case, ants appear to head instead towards the regions where the skyline appears lower than the height recorded in their memory, which generally leads them closer to a familiar location. How ants choose between strategies at a given time might be simply based on the degree of familiarity of the panoramic scene currently perceived. Finally, this thesis raises questions about the nature of ant memories. Past studies proposed that ants memorise a succession of discrete 2D 'snapshots' of their surroundings. Contrastingly, results obtained here show that knowledge from the end of a foraging route (15 m) impacts strongly on the behaviour at the beginning of the route, suggesting that the visual knowledge of a whole foraging route may be compacted into a single holistic memory. Accordingly, repetitive training on the exact same route clearly affects the ants' behaviour, suggesting that the memorised information is processed and not 'obtained at once'. While navigating along their familiar route, ants' visual system is continually stimulated by a slowly evolving scene, and learning a general pattern of stimulation rather than storing independent but very similar snapshots appears a reasonable hypothesis to explain navigation on a natural scale; such learning works remarkably well with neural networks. Nonetheless, what the precise nature of ants' visual memories is and how elaborated they are remain wide open question. Overall, my thesis tackles the nature of ants' perception and memory as well as how both are processed together to output an appropriate navigational response. These results are discussed in the light of comparative cognition. Both vertebrates and insects have resolved the same problem of navigating efficiently in the world. In light of Darwin's theory of evolution, there is no a priori reason to think that there is a clear division between cognitive mechanisms of different species. The actual gap between insect and vertebrate cognitive sciences may result more from different approaches rather than real differences. Research on insect navigation has been approached with a bottom-up philosophy, one that examines how simple mechanisms can produce seemingly complex behaviour. Such parsimonious solutions, like the ones explored in the present thesis, can provide useful baseline hypotheses for navigation in other larger-brained animals, and thus contribute to a more truly comparative cognition

A model of ant route navigation driven by scene familiarity

In this paper we propose a model of visually guided route navigation in ants that captures the known properties of real behaviour whilst retaining mechanistic simplicity and thus biological plausibility. For an ant, the coupling of movement and viewing direction means that a familiar view specifies a familiar direction of movement. Since the views experienced along a habitual route will be more familiar, route navigation can be re-cast as a search for familiar views. This search can be performed with a simple scanning routine, a behaviour that ants have been observed to perform. We test this proposed route navigation strategy in simulation, by learning a series of routes through visually cluttered environments consisting of objects that are only distinguishable as silhouettes against the sky. In the first instance we determine view familiarity by exhaustive comparison with the set of views experienced during training. In further experiments we train an artificial neural network to perform familiarity discrimination using the training views. Our results indicate that, not only is the approach successful, but also that the routes that are learnt show many of the characteristics of the routes of desert ants. As such, we believe the model represents the only detailed and complete model of insect route guidance to date. What is more, the model provides a general demonstration that visually guided routes can be produced with parsimonious mechanisms that do not specify when or what to learn, nor separate routes into sequences of waypoints

A situated and embodied model of ant route navigation

This abstract summarises a model of route navigation inspired by the behaviour of ants presented fully in Baddeley et al. (2012). The ant's embodiment coupled with an innate scanning behaviour means that robust route navigation can be achieved by a parsimonious biologically plausible algorithm

Robot Mapping and Navigation in Real-World Environments

Robots can perform various tasks, such as mapping hazardous sites, taking part in search-and-rescue scenarios, or delivering goods and people. Robots operating in the real world face many challenges on the way to the completion of their mission. Essential capabilities required for the operation of such robots are mapping, localization and navigation. Solving all of these tasks robustly presents a substantial difficulty as these components are usually interconnected, i.e., a robot that starts without any knowledge about the environment must simultaneously build a map, localize itself in it, analyze the surroundings and plan a path to efficiently explore an unknown environment. In addition to the interconnections between these tasks, they highly depend on the sensors used by the robot and on the type of the environment in which the robot operates. For example, an RGB camera can be used in an outdoor scene for computing visual odometry, or to detect dynamic objects but becomes less useful in an environment that does not have enough light for cameras to operate. The software that controls the behavior of the robot must seamlessly process all the data coming from different sensors. This often leads to systems that are tailored to a particular robot and a particular set of sensors. In this thesis, we challenge this concept by developing and implementing methods for a typical robot navigation pipeline that can work with different types of the sensors seamlessly both, in indoor and outdoor environments. With the emergence of new range-sensing RGBD and LiDAR sensors, there is an opportunity to build a single system that can operate robustly both in indoor and outdoor environments equally well and, thus, extends the application areas of mobile robots. The techniques presented in this thesis aim to be used with both RGBD and LiDAR sensors without adaptations for individual sensor models by using range image representation and aim to provide methods for navigation and scene interpretation in both static and dynamic environments. For a static world, we present a number of approaches that address the core components of a typical robot navigation pipeline. At the core of building a consistent map of the environment using a mobile robot lies point cloud matching. To this end, we present a method for photometric point cloud matching that treats RGBD and LiDAR sensors in a uniform fashion and is able to accurately register point clouds at the frame rate of the sensor. This method serves as a building block for the further mapping pipeline. In addition to the matching algorithm, we present a method for traversability analysis of the currently observed terrain in order to guide an autonomous robot to the safe parts of the surrounding environment. A source of danger when navigating difficult to access sites is the fact that the robot may fail in building a correct map of the environment. This dramatically impacts the ability of an autonomous robot to navigate towards its goal in a robust way, thus, it is important for the robot to be able to detect these situations and to find its way home not relying on any kind of map. To address this challenge, we present a method for analyzing the quality of the map that the robot has built to date, and safely returning the robot to the starting point in case the map is found to be in an inconsistent state. The scenes in dynamic environments are vastly different from the ones experienced in static ones. In a dynamic setting, objects can be moving, thus making static traversability estimates not enough. With the approaches developed in this thesis, we aim at identifying distinct objects and tracking them to aid navigation and scene understanding. We target these challenges by providing a method for clustering a scene taken with a LiDAR scanner and a measure that can be used to determine if two clustered objects are similar that can aid the tracking performance. All methods presented in this thesis are capable of supporting real-time robot operation, rely on RGBD or LiDAR sensors and have been tested on real robots in real-world environments and on real-world datasets. All approaches have been published in peer-reviewed conference papers and journal articles. In addition to that, most of the presented contributions have been released publicly as open source software

Visual homing in field crickets and desert ants: a comparative behavioural and modelling study

Visually guided navigation represents a long standing goal in robotics. Insights may be drawn from various insect species for which visual information has been shown sufficient for navigation in complex environments, however the generality of visual homing abilities across insect species remains unclear. Furthermore variousmodels have been proposed as strategies employed by navigating insects yet comparative studies across models and species are lacking. This work addresses these questions in two insect species not previously studied: the field cricket Gryllus bimaculatus for which almost no navigational data is available; and the European desert ant Cataglyphis velox, a relation of the African desert ant Cataglyphis bicolor which has become a model species for insect navigation studies. The ability of crickets to return to a hidden target using surrounding visual cues was tested using an analogue of the Morris water-maze, a standard paradigm for spatial memory testing in rodents. Crickets learned to re-locate the hidden target using the provided visual cues, with the best performance recorded when a natural image was provided as stimulus rather than clearly identifiable landmarks. The role of vision in navigation was also observed for desert ants within their natural habitat. Foraging ants formed individual, idiosyncratic, visually guided routes through their cluttered surroundings as has been reported in other ant species inhabiting similar environments. In the absence of other cues ants recalled their route even when displaced along their path indicating that ants recall previously visited places rather than a sequence of manoeuvres. Image databases were collected within the environments experienced by the insects using custompanoramic cameras that approximated the insect eye viewof the world. Six biologically plausible visual homing models were implemented and their performance assessed across experimental conditions. The models were first assessed on their ability to replicate the relative performance across the various visual surrounds in which crickets were tested. That is, best performance was sought with the natural scene, followed by blank walls and then the distinct landmarks. Only two models were able to reproduce the pattern of results observed in crickets: pixel-wise image difference with RunDown and the centre of mass average landmark vector. The efficacy of models was then assessed across locations in the ant habitat. A 3D world was generated from the captured images providing noise free and high spatial resolution images asmodel input. Best performancewas found for optic flow and image difference based models. However in many locations the centre of mass average landmark vector failed to provide reliable guidance. This work shows that two previously unstudied insect species can navigate using surrounding visual cues alone. Moreover six biologically plausible models of visual navigation were assessed in the same environments as the insects and only an image difference based model succeeded in all experimental conditions