Laser-camera data discrepancies and reliable perception in outdoor robotics

This work aims to promote integrity in autonomous perceptual systems, with a focus on outdoor unmanned ground vehicles equipped with a camera and a 2D laser range finder. A method to check for inconsistencies between the data provided by these two heterogeneous sensors is proposed and discussed. First, uncertainties in the estimated transformation between the laser and camera frames are evaluated and propagated up to the projection of the laser points onto the image. Then, for each pair of laser scan-camera image acquired, the information at corners of the laser scan is compared with the content of the image, resulting in a likelihood of correspondence. The result of this process is then used to validate segments of the laser scan that are found to be consistent with the image, while inconsistent segments are rejected. Experimental results illustrate how this technique can improve the reliability of perception in challenging environmental conditions, such as in the presence of airborne dust.

Towards reliable perception for unmanned ground vehicles in challenging conditions

This work aims to promote reliability and integrity in autonomous perceptual systems, with a focus on outdoor unmanned ground vehicle (UGV) autonomy. For this purpose, a comprehensive UGV system, comprising many different exteroceptive and proprioceptive sensors has been built. The first contribution of this work is a large, accurately calibrated and synchronised, multi-modal data-set, gathered in controlled environmental conditions, including the presence of dust, smoke and rain. The data have then been used to analyse the effects of such challenging conditions on perception and to identify common perceptual failures. The second contribution is a presentation of methods for mitigating these failures to promote perceptual integrity in adverse environmental conditions.

Laser-to-radar sensing redundancy for resilient perception in adverse environmental conditions

This paper presents an approach to promote the integrity of perception systems for outdoor unmanned ground vehicles (UGV) operating in challenging environmental conditions (presence of dust or smoke). The proposed technique automatically evaluates the consistency of the data provided by two sensing modalities: a 2D laser range finder and a millimetre-wave radar, allowing for perceptual failure mitigation. Experimental results, obtained with a UGV operating in rural environments, and an error analysis validate the approach.

Laser-radar data fusion with Gaussian Process Implicit Surfaces

This work considers the problem of building high-fidelity 3D representations of the environment from sensor data acquired by mobile robots. Multi-sensor data fusion allows for more complete and accurate representations, and for more reliable perception, especially when different sensing modalities are used. In this paper, we propose a thorough experimental analysis of the performance of 3D surface reconstruction from laser and mm-wave radar data using Gaussian Process Implicit Surfaces (GPIS), in a realistic field robotics scenario. We first analyse the performance of GPIS using raw laser data alone and raw radar data alone, respectively, with different choices of covariance matrices and different resolutions of the input data. We then evaluate and compare the performance of two different GPIS fusion approaches. The first, state-of-the-art approach directly fuses raw data from laser and radar. The alternative approach proposed in this paper first computes an initial estimate of the surface from each single source of data, and then fuses these two estimates. We show that this method outperforms the state of the art, especially in situations where the sensors react differently to the targets they perceive.

Towards discrimination of challenging conditions for UGVs with visual and infrared sensors

This work aims to contribute to reliability and integrity in perceptual systems of autonomous ground vehicles. Information theoretic based metrics to evaluate the quality of sensor data are proposed and applied to visual and infrared camera images. The contribution of the proposed metrics to the discrimination of challenging conditions is discussed and illustrated with the presence of airborne dust and smoke.

Combining multiple sensor modalities for a localisation robust to smoke

This paper proposes an approach to obtain a localisation that is robust to smoke by exploiting multiple sensing modalities: visual and infrared (IR) cameras. This localisation is based on a state-of-the-art visual SLAM algorithm. First, we show that a reasonably accurate localisation can be obtained in the presence of smoke by using only an IR camera, a sensor that is hardly affected by smoke, contrary to a visual camera (operating in the visible spectrum). Second, we demonstrate that improved results can be obtained by combining the information from the two sensor modalities (visual and IR cameras). Third, we show that by detecting the impact of smoke on the visual images using a data quality metric, we can anticipate and mitigate the degradation in performance of the localisation by discarding the most affected data. The experimental validation presents multiple trajectories estimated by the various methods considered, all thoroughly compared to an accurate dGPS/INS reference.

Robust multiple-sensing-modality data fusion using Gaussian Process Implicit Surfaces

The ability to build high-fidelity 3D representations of the environment from sensor data is critical for autonomous robots. Multi-sensor data fusion allows for more complete and accurate representations. Furthermore, using distinct sensing modalities (i.e. sensors using a different physical process and/or operating at different electromagnetic frequencies) usually leads to more reliable perception, especially in challenging environments, as modalities may complement each other. However, they may react differently to certain materials or environmental conditions, leading to catastrophic fusion. In this paper, we propose a new method to reliably fuse data from multiple sensing modalities, including in situations where they detect different targets. We first compute distinct continuous surface representations for each sensing modality, with uncertainty, using Gaussian Process Implicit Surfaces (GPIS). Second, we perform a local consistency test between these representations, to separate consistent data (i.e. data corresponding to the detection of the same target by the sensors) from inconsistent data. The consistent data can then be fused together, using another GPIS process, and the rest of the data can be combined as appropriate. The approach is first validated using synthetic data. We then demonstrate its benefit using a mobile robot, equipped with a laser scanner and a radar, which operates in an outdoor environment in the presence of large clouds of airborne dust and smoke.

Characterisation of the Delphi Electronically Scanning Radar for robotics applications

Mm-wave radars have an important role to play in field robotics for applications that require reliable perception in challenging environmental conditions. This paper presents an experimental characterisation of the Delphi Electronically Scanning Radar (ESR) for mobile robotics applications. The performance of the sensor is evaluated in terms of detection ability and accuracy, for varying factors including: sensor temperature, time, target’s position, speed, shape and material. We also evaluate the sensor’s target separability performance.

Multisensorial obstacles detection and classification for ADAS and autonomous driving

A reliable perception of the real world is a key-feature for an autonomous vehicle and the Advanced Driver Assistance Systems (ADAS). Obstacles detection (OD) is one of the main components for the correct reconstruction of the dynamic world. Historical approaches based on stereo vision and other 3D perception technologies (e.g. LIDAR) have been adapted to the ADAS first and autonomous ground vehicles, after, providing excellent results. The obstacles detection is a very broad field and this domain counts a lot of works in the last years. In academic research, it has been clearly established the essential role of these systems to realize active safety systems for accident prevention, reflecting also the innovative systems introduced by industry. These systems need to accurately assess situational criticalities and simultaneously assess awareness of these criticalities by the driver; it requires that the obstacles detection algorithms must be reliable and accurate, providing: a real-time output, a stable and robust representation of the environment and an estimation independent from lighting and weather conditions. Initial systems relied on only one exteroceptive sensor (e.g. radar or laser for ACC and camera for LDW) in addition to proprioceptive sensors such as wheel speed and yaw rate sensors. But, current systems, such as ACC operating at the entire speed range or autonomous braking for collision avoidance, require the use of multiple sensors since individually they can not meet these requirements. It has led the community to move towards the use of a combination of them in order to exploit the benefits of each one. Pedestrians and vehicles detection are ones of the major thrusts in situational criticalities assessment, still remaining an active area of research. ADASs are the most prominent use case of pedestrians and vehicles detection. Vehicles should be equipped with sensing capabilities able to detect and act on objects in dangerous situations, where the driver would not be able to avoid a collision. A full ADAS or autonomous vehicle, with regard to pedestrians and vehicles, would not only include detection but also tracking, orientation, intent analysis, and collision prediction. The system detects obstacles using a probabilistic occupancy grid built from a multi-resolution disparity map. Obstacles classification is based on an AdaBoost SoftCascade trained on Aggregate Channel Features. A final stage of tracking and fusion guarantees stability and robustness to the result.

Assessing the overuse of antibiotics in children with URTIs in Saudi Arabia : development of the parental perception on antibiotics scale (PAPA scale)

Background: Antibiotic overuse is influenced by several factors that can only be measured using a valid and reliable psychosocial measurement instrument. This study aims to establish translation and early stage validation of an instrument recently developed by this research team to measure factors influencing the overuse of antibiotics in children with upper respiratory tract infections in Saudi Arabia. Method: The content evaluation panel was composed of area experts approached using the Delphi Technique. Experts were provided with the questionnaires iteratively, on a three-round basis until consensus on the relevance of items was reached independently. Translation was achieved by adapting Brislin’s model of translation. Results: After going through the iterative process with the experts, consensus was reached to 58 items (including demographics). Experts also pointed out some issues related to ambiguity and redundancy in some items. A final Arabic version was produced from the translation process. Conclusion: This study produced preliminary validation of the developed instrument from the experts’ contributions. Then, the instrument was translated from English to Arabic. The instrument will undergo further validation steps in the future, such as construct validity.

Assessing the overuse of antibiotics in children in Saudi Arabia : validation of the parental perception on antibiotics scale (PAPA scale)

Background Antibiotics overuse is a global public health issue influenced by several factors, of which some are parent-related psychosocial factors that can only be measured using valid and reliable psychosocial measurement instruments. The PAPA scale was developed to measure these factors and the content validity of this instrument was assessed. Aim This study further validated the recently developed instrument in terms of (1) face validity and (2) construct validity including: deciding the number and nature of factors, and item selection. Methods Questionnaires were self-administered to parents of children between the ages of 0 and 12 years old. Parents were conveniently recruited from schools’ parental meetings in the Eastern Province, Saudi Arabia. Face validity was assessed with regards to questionnaire clarity and unambiguity. Construct validity and item selection processes were conducted using Exploratory factor analysis. Results Parallel analysis and Exploratory factor analysis using principal axis factoring produced six factors in the developed instrument: knowledge and beliefs, behaviours, sources of information, adherence, awareness about antibiotics resistance, and parents’ perception regarding doctors’ prescribing behaviours. Reliability was assessed (Cronbach’s alpha = 0.78) which demonstrates the instrument as being reliable. Conclusion The ‘factors’ produced in this study coincide with the constructs contextually identified in the development phase of other instruments used to study antibiotic use. However, no other study considering perceptions of antibiotic use had gone beyond content validation of such instruments. This study is the first to constructively validate the factors underlying perceptions regarding antibiotic use in any population and in parents in particular.

Fuzzy-DL perception for multi-robot systems

For future planetary robot missions, multi-robot-systems can be considered as a suitable platform to perform space mission faster and more reliable. In heterogeneous robot teams, each robot can have different abilities and sensor equipment. In this paper we describe a lunar demonstration scenario where a team of mobile robots explores an unknown area and identifies a set of objects belonging to a lunar infrastructure. Our robot team consists of two exploring scout robots and a mobile manipulator. The mission goal is to locate the objects within a certain area, to identify the objects, and to transport the objects to a base station. The robots have a different sensor setup and different capabilities. In order to classify parts of the lunar infrastructure, the robots have to share the knowledge about the objects. Based on the different sensing capabilities, several information modalities have to be shared and combined by the robots. In this work we propose an approach using spatial features and a fuzzy logic based reasoning for distributed object classification.

Using a brief questionnaire to assess the overall perception of basketball spectators

Purpose – The purpose of this paper is twofold. The first aim is to obtain a valid and reliable instrument for the holistic analysis of sporting events, and the second is to test a causal model in which future intentions depend on spectators’ perceptions of quality, satisfaction, and value of these events. Design/methodology/approach – A total of 493 spectators of a professional basketball team in the Spanish ACB league responded to a survey to measure the overall performance of the sporting event service. Exploratory factor analysis and further confirmatory factor analysis using structural equation models provides the methodology for testing the reliability and validity of the instrument. Findings – The scales have adequate reliability and validity indices. The path model explains 35.8 percent of the variance in future intentions, 54.0 percent in perceived value, and 49.5 percent in spectators’ satisfaction. Quality proves a better predictor of perceived value than satisfaction. Both perceived value and satisfaction have a similar weight in predicting spectators’ future intentions. The data indicate that quality has an effect on spectators’ future intentions, by altering their perceptions of value and satisfaction. Research limitations/implications – The research findings are somewhat limited, due to the sample consisting entirely of spectators of a single team in the Spanish ACB league. Practical implications – Managers can use these findings to develop loyalty strategies by creating service value and increasing spectators’ satisfaction through quality improvements. Originality/value – This study contributes to the literature on service quality by providing an overall measure to assess service in professional sporting events in a Latin-American context.

Why walkers slip: shine is not a reliable cue for slippery ground.

In a series of four studies, we investigated the visual cues that walkers use to predict slippery ground surfaces and tested whether visual information is reliable for specifying low-friction conditions. In Study 1, 91% of participants surveyed responded that they would use shine to identify upcoming slippery ground. Studies 2-4 confirmed participants' reliance on shine to predict slip. Participants viewed ground surfaces varying in gloss, paint color, and viewing distance under indoor and outdoor lighting conditions. Shine and slip ratings and functional walking judgments were related to surface gloss level and to surface coefficient of friction (COF). However, judgments were strongly affected by surface color, viewing distance, and lighting conditions--extraneous factors that do not change the surface COF. Results suggest that, although walkers rely on shine to predict slippery ground, shine is not a reliable visual cue for friction. Poor visual information for friction may underlie the high prevalence of friction-related slips and falls.

The reliability of an isometric test based on constant perception of effort

To date there is no documented procedure to extrapolate findings of an isometric nature to a whole body performance setting. The purpose of this study was to quantify the reliability of perceived exertion to control neuromuscular output during an isometric contraction. 21 varsity athletes completed a maximal voluntary contraction and a 2 min constant force contraction at both the start and end of the study. Between pre and post testing all participants completed a 2 min constant perceived exertion contraction once a day for 4 days. Intra-class correlation coefficient (R=O.949) and standard error of measurement (SEM=5.12 Nm) concluded that the isometric contraction was reliable. Limits of agreement demonstrated only moderate initial reliability, yet with smaller limits towards the end of 4 training sessions. In conclusion, athlete's na"ive to a constant effort isometric contraction will produce reliable and acceptably stable results after 1 familiarization sessions has been completed.