A Distributed Robotic Vision Service

Robotic vision is limited by line of sight and onboard camera capabilities. Robots can acquire video or images from remote cameras, but processing additional data has a computational burden. This paper applies the Distributed Robotic Vision Service, DRVS, to robot path planning using data outside line-of-sight of the robot. DRVS implements a distributed visual object detection service to distributes the computation to remote camera nodes with processing capabilities. Robots request task-specific object detection from DRVS by specifying a geographic region of interest and object type. The remote camera nodes perform the visual processing and send the high-level object information to the robot. Additionally, DRVS relieves robots of sensor discovery by dynamically distributing object detection requests to remote camera nodes. Tested over two different indoor path planning tasks DRVS showed dramatic reduction in mobile robot compute load and wireless network utilization.

Beyond Gauss: Image-set matching on the Riemannian manifold of PDFs

State-of-the-art image-set matching techniques typically implicitly model each image-set with a Gaussian distribution. Here, we propose to go beyond these representations and model image-sets as probability distribution functions (PDFs) using kernel density estimators. To compare and match image-sets, we exploit Csiszar´ f-divergences, which bear strong connections to the geodesic distance defined on the space of PDFs, i.e., the statistical manifold. Furthermore, we introduce valid positive definite kernels on the statistical manifold, which let us make use of more powerful classification schemes to match image-sets. Finally, we introduce a supervised dimensionality reduction technique that learns a latent space where f-divergences reflect the class labels of the data. Our experiments on diverse problems, such as video-based face recognition and dynamic texture classification, evidence the benefits of our approach over the state-of-the-art image-set matching methods.

Deploying speaker verification techniques to vision-based RPA detect and avoid

Detect and Avoid (DAA) technology is widely acknowledged as a critical enabler for unsegregated Remote Piloted Aircraft (RPA) operations, particularly Beyond Visual Line of Sight (BVLOS). Image-based DAA, in the visible spectrum, is a promising technological option for addressing the challenges DAA presents. Two impediments to progress for this approach are the scarcity of available video footage to train and test algorithms, in conjunction with testing regimes and specifications which facilitate repeatable, statistically valid, performance assessment. This paper includes three key contributions undertaken to address these impediments. In the first instance, we detail our progress towards the creation of a large hybrid collision and near-collision encounter database. Second, we explore the suitability of techniques employed by the biometric research community (Speaker Verification and Language Identification), for DAA performance optimisation and assessment. These techniques include Detection Error Trade-off (DET) curves, Equal Error Rates (EER), and the Detection Cost Function (DCF). Finally, the hybrid database and the speech-based techniques are combined and employed in the assessment of a contemporary, image based DAA system. This system includes stabilisation, morphological filtering and a Hidden Markov Model (HMM) temporal filter.

Effect of gaze position and blur on stepping accuracy in older adults.

Purpose: To examine the effects of gaze position and optical blur, similar to that used in multifocal corrections, on stepping accuracy for a precision stepping task among older adults. Methods: Nineteen healthy older adults (mean age, 71.6 +/- 8.8 years) with normal vision performed a series of precision stepping tasks onto a fixed target. The stepping tasks were performed using a repeated-measures design for three gaze positions (fixating on the stepping target as well as 30 and 60 cm farther forward of the stepping target) and two visual conditions (best-corrected vision and with +2.50DS blur). Participants' gaze position was tracked using a head-mounted eye tracker. Absolute, anteroposterior, and mediolateral foot placement errors and within-subject foot placement variability were calculated from the locations of foot and floor-mounted retroreflective markers captured by flash photography of the final foot position. Results: Participants made significantly larger absolute and anteroposterior foot placement errors and exhibited greater foot placement variability when their gaze was directed farther forward of the stepping target. Blur led to significantly increased absolute and anteroposterior foot placement errors and increased foot placement variability. Furthermore, blur differentially increased the absolute and anteroposterior foot placement errors and variability when gaze was directed 60 cm farther forward of the stepping target. Conclusions: Increasing gaze position farther ahead from stepping locations and the presence of blur negatively impact the stepping accuracy of older adults. These findings indicate that blur, similar to that used in multifocal corrections, has the potential to increase the risk of trips and falls among older populations when negotiating challenging environments where precision stepping is required, particularly as gaze is directed farther ahead from stepping locations when walking.

Place categorization and semantic mapping on a mobile robot

In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot with-out environment-specific training. Motivated by their ongoing success in various visual recognition tasks, we build our system upon a state-of-the-art convolutional network. We overcome its closed-set limitations by complementing the network with a series of one-vs-all classifiers that can learn to recognize new semantic classes online. Prior domain knowledge is incorporated by embedding the classification system into a Bayesian filter framework that also ensures temporal coherence. We evaluate the classification accuracy of the system on a robot that maps a variety of places on our campus in real-time. We show how semantic information can boost robotic object detection performance and how the semantic map can be used to modulate the robot’s behaviour during navigation tasks. The system is made available to the community as a ROS module.