Bio-inspired visual memory for robot cognitive map building and scene recognition

Rebai, Karima; Azouaoui, Ouahiba; Achour, Nouara

doi:10.1109/iros.2012.6385493

Cited by 11 publications

(7 citation statements)

References 18 publications

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“…Cognitive maps have been applied in scene recognition. For example, Rebai et al, presented an approach for indoor navigation that builds a visual memory that allows spatial recognition without storing visual information [94]. This approach builds the visual memory using Fuzzy ART, a model capable of rapid stable leaning of recognition categories in response to arbitrary sequences [95].…”

Section: Cognitive Mapsmentioning

confidence: 99%

Semantic Information for Robot Navigation: A Survey

et al. 2020

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There is a growing trend in robotics for implementing behavioural mechanisms based on human psychology, such as the processes associated with thinking. Semantic knowledge has opened new paths in robot navigation, allowing a higher level of abstraction in the representation of information. In contrast with the early years, when navigation relied on geometric navigators that interpreted the environment as a series of accessible areas or later developments that led to the use of graph theory, semantic information has moved robot navigation one step further. This work presents a survey on the concepts, methodologies and techniques that allow including semantic information in robot navigation systems. The techniques involved have to deal with a range of tasks from modelling the environment and building a semantic map, to including methods to learn new concepts and the representation of the knowledge acquired, in many cases through interaction with users. As understanding the environment is essential to achieve high-level navigation, this paper reviews techniques for acquisition of semantic information, paying attention to the two main groups: human-assisted and autonomous techniques. Some state-of-the-art semantic knowledge representations are also studied, including ontologies, cognitive maps and semantic maps. All of this leads to a recent concept, semantic navigation, which integrates the previous topics to generate high-level navigation systems able to deal with real-world complex situations.

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Section: Cognitive Mapsmentioning

confidence: 99%

Semantic Information for Robot Navigation: A Survey

et al. 2020

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“…In vertebrates, the hippocampus helps to solve this memory problem by maintaining a spatial map of its organism's surroundings [24]. Biologically inspired vision systems seek to reproduce the functionality of such spatial maps [25] and visual short-term memory [26]. Kinaesthesia, on the other hand, allows organisms to estimate the relative locations and orientations of body parts.…”

Section: A Visual Memorymentioning

confidence: 99%

A Neuro-Inspired Computational Model for a Visually Guided Robotic Lamprey Using Frame and Event Based Cameras

Youssef

Mutlu

Bayat

et al. 2020

IEEE Robot. Autom. Lett.

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The computational load associated with computer vision is often prohibitive, and limits the capacity for on-board image analysis in compact mobile robots. Replicating the kind of feature detection and neural processing that animals excel at remains a challenge in most biomimetic aquatic robots. Event-driven sensors use a biologically inspired sensing strategy to eliminate the need for complete frame capture. Systems employing event-driven cameras enjoy reduced latencies, power consumption, bandwidth, and benefit from a large dynamic range. However, to the best of our knowledge, no work has been done to evaluate the performance of these devices in underwater robotics. This work proposes a robotic lamprey design capable of supporting computer vision, and uses this system to validate a computational neuron model for driving anguilliform swimming. The robot is equipped with two different types of cameras: framebased and event-based cameras. These were used to stimulate the neural network, yielding goal-oriented swimming. Finally, a study is conducted comparing the performance of the computational model when driven by the two different types of camera. It was observed that event-based cameras improved the accuracy of swimming trajectories and led to significant improvements in the rate at which visual inputs were processed by the network.

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“…For example, Suenderhauf and Protzel formulated dynamics of pose cells in terms of a Bayes filter, which they named Causal Update Filter (CUF) and used a TORO pose graph algorithm for experience mapping [24]; however, no significant improvements are achieved compared to Rat-SLAM. Rebai et al used a Fuzzy ART network to capture the properties of spatial view cells in primates [20]. The network is trained incrementally on the quantized local histograms of hue and saturation.…”

Section: Related Workmentioning

confidence: 99%

Gist+RatSLAM: An Incremental Bio-inspired Place Recognition Front-End for RatSLAM

Kazmi

Mertsching

2016

Proceedings of the 9th EAI International Conference on Bio-Inspired Information and Communications Technologies (Formerly BIONE

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There exists ample research exploiting cognitive processes for robot localization and mapping, for instance RatSLAM [10]. In this regard, tasks such as visual perception and recognition, which are primarily governed by visual and perirhinal cortices, receive a little attention. To bridge this gap, we present a novel bio-inspired place recognition front-end for the RatSLAM system. Our algorithm uses Gist features to obtain the perceptual structure of the scenes and employs a modified growing self-organizing map (GSOM) to model the behavior of the cells found in perirhinal cortex, called recency and familiarity neurons [6]. This enables an online learning and recognition of the places without acquiring apriori knowledge of the environment. The experiments carried out on the standard St. Lucia dataset demonstrate that on average our approach achieves almost 10% improvement (in F1-Score); it is able to correctly flag the visited and unvisited places even for noisy and blurred visual inputs. The results show that the proposed method reaches fast convergence and utilizes a smaller number of cells (consumes less physical memory) to represent the traversed path compared to the RatSLAM approach.descriptor to obtain the structural formation of the scenes. This information is later fed to the modified growing selforganizing map (GSOM), a type of neural network, to organize the places based on their perceptual distances. A comprehensive evaluation of the proposed approach on the standard St. Lucia dataset is presented in Section 4, including comparison with the state-of-the-art RatSLAM algorithm. The results show that the proposed approach performs better than existing front-end module (data association) of the RatSLAM system and imposes less consumption of physical memory. Finally, we conclude the discussion in Section 5 with the possible directions of future work.

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Bio-inspired visual memory for robot cognitive map building and scene recognition

Cited by 11 publications

References 18 publications

Semantic Information for Robot Navigation: A Survey

Semantic Information for Robot Navigation: A Survey

A Neuro-Inspired Computational Model for a Visually Guided Robotic Lamprey Using Frame and Event Based Cameras

Gist+RatSLAM: An Incremental Bio-inspired Place Recognition Front-End for RatSLAM

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