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Being able to explore an environment and understand the location and type of all objects therein is important for indoor robotic platforms that must interact closely with humans. However, it is difficult to evaluate progress in this area due to a lack of standardized testing which is limited due to the need for active robot agency and perfect object ground-truth. To help provide a standard for testing scene understanding systems, we present a new robot vision scene understanding challenge using simulation to enable repeatable experiments with active robot agency. We provide two challenging task types, three difficulty levels, five simulated environments and a new evaluation measure for evaluating 3D cuboid object maps. Our aim is to drive state-of-the-art research in scene understanding through enabling evaluation and comparison of active robotic vision systems.

Abstract-This paper presents image-based navigation from an image memory using a combination of line segments and feature points. The environment is represented by a set of key images, which are acquired during a prior mapping phase that defines the path to be followed during the navigation. The switching of key images is done exploiting the common line segments and feature points between the current acquired image and the nearby key images. Based on the key images and the current image, a control law is derived for computing the rotational velocity of a mobile robot during its visual navigation. Using our approach, real-time navigation has been performed in real indoor environment with a Pioneer 3-DX equipped with an on-board perspective camera and the humanoid robot Pepper without the need of accurate mapping and localization nor of 3D reconstruction. We also show that the combination of points and lines increases the number of features that helps in robust and successful navigation especially in those regions where few points or lines can be detected and tracked/matched.

Mobile phone induced electromagnetic field (MPEMF) as well as chanting of Vedic mantra 'OM' has been shown to affect cognition and brain haemodynamics, but findings are still inconclusive. Twenty right-handed healthy teenagers (eight males and 12 females) in the age range of 18.25 ± 0.44 years were randomly divided into four groups: (1) MPONOM (mobile phone 'ON' followed by 'OM' chanting); (2) MPOFOM (mobile phone 'OFF' followed by 'OM' chanting); (3) MPONSS (mobile phone 'ON' followed by 'SS' chanting); and (4) MPOFSS (mobile phone 'OFF' followed by 'SS' chanting). Brain haemodynamics during Stroop task were recorded using a 64-channel fNIRS device at three points of time: (1) baseline, (2) after 30 min of MPON/OF exposure, and (3) after 5 min of OM/SS chanting. RM-ANOVA was applied to perform within- and between-group comparisons, respectively. Between-group analysis revealed that total scores on incongruent Stroop task were significantly better after OM as compared to SS chanting (MPOFOM vs MPOFSS), pre-frontal activation was significantly lesser after OM as compared to SS chanting in channel 13. There was no significant difference between MPON and MPOF conditions for Stroop performance, as well as brain haemodynamics. These findings need confirmation through a larger trial in future.

Computer-aided diagnosis is developed for assessment of allergic rhinitis/rhinoconjunctivitis measuring the relative redness of sclera under application of allergen solution. Images of the patient's eye are taken using a commercial digital camera. The iris is robustly localized using a gradient-based Hough circle transform. From the center of the pupil, the region of interest within the sclera is extracted using geometric anatomy-based apriori information. The red color pixels are extracted thresholding in the hue, saturation and value color space. Then, redness is measured by taking mean of saturation projected into zero hue. Evaluation is performed with 98 images taken from 14 subjects, 8 responders and 6 non-responders, which were classified according to an experienced otorhinolaryngologist. Provocation is performed with 100, 1,000 and 10,000 AU/ml allergic solution and normalized to control images without provocation. The evaluation yields relative redness of 1.01, 1.05, 1.30 and 0.95, 1.00, 0.96 for responders and non-responders, respectively. Variations in redness measurements were analyzed according to alteration of parameters of the image processing chain proving stability and robustness of our approach. The results indicate that the method improves visual inspection and may be suitable as reliable surrogate endpoint in controlled clinical trials.

We present a platform to foster research in active scene understanding, consisting of high-fidelity simulated environments and a simple yet powerful API that controls a mobile robot in simulation and reality. In contrast to static, pre-recorded datasets that focus on the perception aspect of scene understanding, agency is a top priority in our work. We provide three levels of robot agency, allowing users to control a robot at varying levels of difficulty and realism. While the most basic level provides pre-defined trajectories and ground-truth localisation, the more realistic levels allow us to evaluate integrated behaviours comprising perception, navigation, exploration and SLAM. In contrast to existing simulation environments, we focus on robust scene understanding research using our environment interface (BenchBot) that provides a simple API for seamless transition between the simulated environments and real robotic platforms. We believe this scaffolded design is an effective approach to bridge the gap between classical static datasets without any agency and the unique challenges of robotic evaluation in reality. Our BenchBot Environments for Active Robotics (BEAR) consist of 25 indoor environments under day and night lighting conditions, a total of 1443 objects to be identified and mapped, and ground-truth 3D bounding boxes for use in evaluation. BEAR website: https://qcr.github.io/dataset/benchbot-bear-data/ .

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