OpenRatSLAM: an open source brain-based SLAM system

Ball, David; Heath, Scott; Wiles, Janet; Wyeth, Gordon; Corke, Peter; Milford, Michael

doi:10.1007/s10514-012-9317-9

Cited by 127 publications

(106 citation statements)

References 23 publications

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“…Accordingly, allocentric (world-centered) representations of location and direction must be anchored to egocentric (bodycentered) sensory information, requiring a transformation between reference frames (Salinas and Abbott, 1995;Pouget and Sejnowski, 1997;Burgess et al, 2001a;Pouget et al, 2002;Byrne et al, 2007;Wilber et al, 2014;Alexander and Nitz, 2015). The difference between sensory input representing an egocentric landmark bearing and an allocentric HD signal can be ignored in models assuming distant orientation cues but becomes obvious in the context of motion parallax with proximal cues and in complex environments comprised of multiple connected arenas.…”

Section: Discussionmentioning

confidence: 99%

“…However, sensory representations are necessarily body-centered (egocentric). The required mapping between allocentric and egocentric reference frames is a complex problem and could be addressed in terms of coordinate transforms in parietal cortex (Salinas and Abbott, 1995;Pouget and Sejnowski, 1997;Pouget et al, 2002) or retrosplenial cortex (Burgess et al, 2001a;Byrne et al, 2007, Wilber et al, 2014, Alexander and Nitz, 2015, but specific implications for the HD system have not yet been explored.…”

Section: Introductionmentioning

confidence: 99%

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Environmental Anchoring of Head Direction in a Computational Model of Retrosplenial Cortex

Bičanski

Burgess

2016

J. Neurosci.

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Allocentric (world-centered) spatial codes driven by path integration accumulate error unless reset by environmental sensory inputs that are necessarily egocentric (body-centered). Previous models of the head direction system avoided the necessary transformation between egocentric and allocentric reference frames by placing visual cues at infinity. Here we present a model of head direction coding that copes with exclusively proximal cues by making use of a conjunctive representation of head direction and location in retrosplenial cortex. Egocentric landmark bearing of proximal cues, which changes with location, is mapped onto this retrosplenial representation. The model avoids distortions due to parallax, which occur in simple models when a single proximal cue card is used, and can also accommodate multiple cues, suggesting how it can generalize to arbitrary sensory environments. It provides a functional account of the anatomical distribution of head direction cells along Papez' circuit, of place-by-direction coding in retrosplenial cortex, the anatomical connection from the anterior thalamic nuclei to retrosplenial cortex, and the involvement of retrosplenial cortex in navigation. In addition to parallax correction, the same mechanism allows for continuity of head direction coding between connected environments, and shows how a head direction representation can be stabilized by a single within arena cue. We also make predictions for drift during exploration of a new environment, the effects of hippocampal lesions on retrosplenial cells, and on head direction coding in differently shaped environments.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Environmental Anchoring of Head Direction in a Computational Model of Retrosplenial Cortex

Bičanski

Burgess

2016

J. Neurosci.

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“…A detailed description of the OpenRatSLAM implementation is provided in Ball et al (2013), which is included in this thesis as an appendix (see Appendix A).…”

Section: Ratslammentioning

confidence: 99%

“…These studies are described in more detail in section 2.4. Figure 2.5: The results of RatSLAM mapping several datasets (reproduced from Ball et al (2013)) of the Oxford New College dataset (see Smith et al (2009)); a) the robot's journey, b) an image from the journey, c) the resulting map; the mapping of St Lucia (see Milford and Wyeth (2008)); d) the robot's journey, e) an image from the journey, f) the resulting map and the mapping of the iRat's Australia maze (see Ball et al (2013)); g) the environment, h) an image from the iRat within the maze, i) the resulting map.…”

Section: Gmappingmentioning

confidence: 99%

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Evolving spatial and temporal lexicons across different cognitive architectures

Heath¹

Self Cite

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Communication between mobile robots requires a transfer of symbols, where each symbol signifies a meaning. However, in typical applications, meaning has been ascribed to the symbols by the engineers that have programmed the robots. This thesis explores an alternative: the use of algorithms and representations that allow mobile robots to evolve a shared set of symbols where the meanings of the symbols are derived from the robots' sensors and cognition.Mobile robots have two important properties that affect the learning of symbols, i) that they are capable of locomotion through space over time; and ii) that they come in many different configurations with different architectures. Previous work has demonstrated that mobile robots can learn shared lexicons to describe space through perceptual referents and referents grounded in cognitive maps. However, open questions remain as to how mobile robots can learn to communicate using temporal terms, and how learning lexicons is affected by different cognitive architectures.The major research question addressed in this thesis is how can mobile robots develop spatial and temporal lexicons across different cognitive architectures? Three facets of language learning are considered particularly important for robots with different cognitive architectures: i) the ability to ground terms in cognition;ii) the ability to ground identical terms in different sensors and cognition for each robot; and iii) the ability to handle referential uncertainty -the difficulty of linking words to meanings within ambiguous contexts.Pairs of mobile robots are used to develop lexicons for spatial and temporal terms and to study each of these abilities. The terms developed by the robots are tested by organizing spatial and temporal tasks and extended to additional terms through grounding transfer.In this thesis, language learning is studied within a framework defined by Peirce's semiotic triangle and building on previous Lingodroid studies. Conversations between robots are used to socially ground symbols within the robots' spatial and temporal cognition. Distributed lexicon tables are used to store links between words and meanings. As the lexicons evolve the words are analyzed for immediate usability, and the final lexicons are analyzed for coherence.Four studies to analyze different aspects of lexicon learning were completed. Study I addressed the aims of learning duration terms using mobile robots and using grounded spatial and temporal language together to perform joint tasks. Identical mobile robots were used to ground terms for time in durations using clocks (time since the last meeting). The robots were able to develop coherent lexicons, and successfully organize future meetings using learned terms.Study II addressed the aim of learning event-based temporal terms using mobile robots. Identical mobile robots were used to ground terms for time in sunlight levels (time of day). The robots required the ability to ground terms in features formed from a brightness level and its derivative. Aga...

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Autonomous Multisensor Calibration and Closed‐loop Fusion for SLAM

Jacobson

Chen

Milford

2014

Journal of Field Robotics

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The vast majority of current robot mapping and navigation systems require specific well‐characterized sensors that may require human‐supervised calibration and are applicable only in one type of environment. Furthermore, if a sensor degrades in performance, either through damage to itself or changes in environmental conditions, the effect on the mapping system is usually catastrophic. In contrast, the natural world presents robust, reasonably well‐characterized solutions to these problems. Using simple movement behaviors and neural learning mechanisms, rats calibrate their sensors for mapping and navigation in an incredibly diverse range of environments and then go on to adapt to sensor damage and changes in the environment over the course of their lifetimes. In this paper, we introduce similar movement‐based autonomous calibration techniques that calibrate place recognition and self‐motion processes as well as methods for online multisensor weighting and fusion. We present calibration and mapping results from multiple robot platforms and multisensory configurations in an office building, university campus, and forest. With moderate assumptions and almost no prior knowledge of the robot, sensor suite, or environment, the methods enable the bio‐inspired RatSLAM system to generate topologically correct maps in the majority of experiments.

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OpenRatSLAM: an open source brain-based SLAM system

Cited by 127 publications

References 23 publications

Environmental Anchoring of Head Direction in a Computational Model of Retrosplenial Cortex

Environmental Anchoring of Head Direction in a Computational Model of Retrosplenial Cortex

Evolving spatial and temporal lexicons across different cognitive architectures

Autonomous Multisensor Calibration and Closed‐loop Fusion for SLAM

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