Pose estimation through cue integration: a neuroscience-inspired approach

Introducing memory and association mechanism into a biologically inspired visual model

A famous biologically inspired hierarchical model (HMAX model), which was proposed recently and corresponds to V1 to V4 of the ventral pathway in primate visual cortex, has been successfully applied to multiple visual recognition tasks. The model is able to achieve a set of position- and scale-tolerant recognition, which is a central problem in pattern recognition. In this paper, based on some other biological experimental evidence, we introduce the memory and association mechanism into the HMAX model. The main contributions of the work are: 1) mimicking the active memory and association mechanism and adding the top down adjustment to the HMAX model, which is the first try to add the active adjustment to this famous model and 2) from the perspective of information, algorithms based on the new model can reduce the computation storage and have a good recognition performance. The new model is also applied to object recognition processes. The primary experimental results show that our method is efficient with a much lower memory requirement.

A robust vision-based sensor fusion approach for real-time pose estimation

Object pose estimation is of great importance to many applications, such as augmented reality, localization and mapping, motion capture, and visual servoing. Although many approaches based on a monocular camera have been proposed, only a few works have concentrated on applying multicamera sensor fusion techniques to pose estimation. Higher accuracy and enhanced robustness toward sensor defects or failures are some of the advantages of these schemes. This paper presents a new Kalman-based sensor fusion approach for pose estimation that offers higher accuracy and precision, and is robust to camera motion and image occlusion, compared to its predecessors. Extensive experiments are conducted to validate the superiority of this fusion method over currently employed vision-based pose estimation algorithms.

A Taxonomy of Vision Systems for Ground Mobile Robots

This paper introduces a taxonomy of vision systems for ground mobile robots. In the last five years, a significant number of relevant papers have contributed to this subject. Firstly, a thorough review of the papers is proposed to discuss and classify both past and the most current approaches in the field. As a result, a global picture of the state of the art of the last five years is obtained. Moreover, the study of the articles is used to put forward a comprehensive taxonomy based on the most up-to-date research in ground mobile robotics. In this sense, the paper aims at being especially helpful to both budding and experienced researchers in the areas of vision systems and mobile ground robots. The taxonomy described is devised from a novel perspective, namely in order to respond to the main questions posed when designing robotic vision systems: why?, what for?, what with?, how?, and where? The answers are derived from the most relevant techniques described in the recent literature, leading in a natural way to a series of classifications that are discussed and contextualized. The article offers a global picture of the state of the art in the area and discovers some promising research lines.

The modular modality frame model: continuous body state estimation and plausibility-weighted information fusion

Humans show admirable capabilities in movement planning and execution. They can perform complex tasks in various contexts, using the available sensory information very effectively. Body models and continuous body state estimations appear necessary to realize such capabilities. We introduce the Modular Modality Frame (MMF) model, which maintains a highly distributed, modularized body model continuously updating, modularized probabilistic body state estimations over time. Modularization is realized with respect to modality frames, that is, sensory modalities in particular frames of reference and with respect to particular body parts. We evaluate MMF performance on a simulated, nine degree of freedom arm in 3D space. The results show that MMF is able to maintain accurate body state estimations despite high sensor and motor noise. Moreover, by comparing the sensory information available in different modality frames, MMF can identify faulty sensory measurements on the fly. In the near future, applications to lightweight robot control should be pursued. Moreover, MMF may be enhanced with neural encodings by introducing neural population codes and learning techniques. Finally, more dexterous goal-directed behavior should be realized by exploiting the available redundant state representations.