A Survey on Odometry for Autonomous Navigation Systems

The development of a navigation system is one of the major challenges in building a fully autonomous platform. Full autonomy requires a dependable navigation capability not only in a perfect situation with clear GPS signals but also in situations, where the GPS is unreliable. Therefore, self-contained odometry systems have attracted much attention recently. This paper provides a general and comprehensive overview of the state of the art in the field of self-contained, i.e., GPS denied odometry systems, and identifies the out-coming challenges that demand further research in future. Self-contained odometry methods are categorized into five main types, i.e., wheel, inertial, laser, radar, and visual, where such categorization is based on the type of the sensor data being used for the odometry. Most of the research in the field is focused on analyzing the sensor data exhaustively or partially to extract the vehicle pose. Different combinations and fusions of sensor data in a tightly/loosely coupled manner and with filtering or optimizing fusion method have been investigated. We analyze the advantages and weaknesses of each approach in terms of different evaluation metrics, such as performance, response time, energy efficiency, and accuracy, which can be a useful guideline for researchers and engineers in the field. In the end, some future research challenges in the field are discussed

Night vision obstacle detection and avoidance based on Bio-Inspired Vision Sensors

Moving towards autonomy, unmanned vehicles rely heavily on state-of-the-art collision avoidance systems (CAS). However, the detection of obstacles especially during night-time is still a challenging task since the lighting conditions are not sufficient for traditional cameras to function properly. Therefore, we exploit the powerful attributes of event-based cameras to perform obstacle detection in low lighting conditions. Event cameras trigger events asynchronously at high output temporal rate with high dynamic range of up to 120 $dB$. The algorithm filters background activity noise and extracts objects using robust Hough transform technique. The depth of each detected object is computed by triangulating 2D features extracted utilising LC-Harris. Finally, asynchronous adaptive collision avoidance (AACA) algorithm is applied for effective avoidance. Qualitative evaluation is compared using event-camera and traditional camera.Comment: Accepted to IEEE SENSORS 202

Asynchronous Corner Tracking Algorithm based on Lifetime of Events for DAVIS Cameras

Event cameras, i.e., the Dynamic and Active-pixel Vision Sensor (DAVIS) ones, capture the intensity changes in the scene and generates a stream of events in an asynchronous fashion. The output rate of such cameras can reach up to 10 million events per second in high dynamic environments. DAVIS cameras use novel vision sensors that mimic human eyes. Their attractive attributes, such as high output rate, High Dynamic Range (HDR), and high pixel bandwidth, make them an ideal solution for applications that require high-frequency tracking. Moreover, applications that operate in challenging lighting scenarios can exploit the high HDR of event cameras, i.e., 140 dB compared to 60 dB of traditional cameras. In this paper, a novel asynchronous corner tracking method is proposed that uses both events and intensity images captured by a DAVIS camera. The Harris algorithm is used to extract features, i.e., frame-corners from keyframes, i.e., intensity images. Afterward, a matching algorithm is used to extract event-corners from the stream of events. Events are solely used to perform asynchronous tracking until the next keyframe is captured. Neighboring events, within a window size of 5x5 pixels around the event-corner, are used to calculate the velocity and direction of extracted event-corners by fitting the 2D planar using a randomized Hough transform algorithm. Experimental evaluation showed that our approach is able to update the location of the extracted corners up to 100 times during the blind time of traditional cameras, i.e., between two consecutive intensity images.Comment: Accepted to 15th International Symposium on Visual Computing (ISVC2020

Online Learning of Wheel Odometry Correction for Mobile Robots with Attention-based Neural Network

Modern robotic platforms need a reliable localization system to operate daily beside humans. Simple pose estimation algorithms based on filtered wheel and inertial odometry often fail in the presence of abrupt kinematic changes and wheel slips. Moreover, despite the recent success of visual odometry, service and assistive robotic tasks often present challenging environmental conditions where visual-based solutions fail due to poor lighting or repetitive feature patterns. In this work, we propose an innovative online learning approach for wheel odometry correction, paving the way for a robust multi-source localization system. An efficient attention-based neural network architecture has been studied to combine precise performances with real-time inference. The proposed solution shows remarkable results compared to a standard neural network and filter-based odometry correction algorithms. Nonetheless, the online learning paradigm avoids the time-consuming data collection procedure and can be adopted on a generic robotic platform on-the-fly

HEMA: A Proposed Robot for Improving Healthcare Access in Underserved Communities

Abstract- Healthcare access is a major challenge in underserved communities, where people often face barriers such as distance, cost, and lack of transportation. HEMA (Horus Expert Medical Assistant Robot) is a new technology with the potential to revolutionize healthcare access in underserved communities by providing basic healthcare services on-site. HEMA is a mobile, affordable, and easy-to-use robot that can collect patient data, diagnose common diseases, and provide basic treatment.HEMA can address the challenges of healthcare access in underserved communities in a number of ways. First, HEMA can provide healthcare services to people who live in remote areas and who may not have access to a traditional healthcare facility. Second, HEMA can provide affordable healthcare services to people who may not be able to afford to pay for healthcare out-of-pocket or who may not have health insurance. Third, HEMA can provide healthcare services to people who may have difficulty traveling to a traditional healthcare facility due to a disability or lack of transportation.HEMA has the potential to make a significant impact on the future of healthcare delivery in underserved communities. By providing basic healthcare services on-site, HEMA can help to improve access to care, reduce disparities in health outcomes, and improve the overall health and well-being of people in underserved communitie

HEMA: A Proposed Robot for Improving Healthcare Access in Underserved Communities

Abstract- Healthcare access is a major challenge in underserved communities, where people often face barriers such as distance, cost, and lack of transportation. HEMA (Horus Expert Medical Assistant Robot) is a new technology with the potential to revolutionize healthcare access in underserved communities by providing basic healthcare services on-site. HEMA is a mobile, affordable, and easy-to-use robot that can collect patient data, diagnose common diseases, and provide basic treatment.HEMA can address the challenges of healthcare access in underserved communities in a number of ways. First, HEMA can provide healthcare services to people who live in remote areas and who may not have access to a traditional healthcare facility. Second, HEMA can provide affordable healthcare services to people who may not be able to afford to pay for healthcare out-of-pocket or who may not have health insurance. Third, HEMA can provide healthcare services to people who may have difficulty traveling to a traditional healthcare facility due to a disability or lack of transportation.HEMA has the potential to make a significant impact on the future of healthcare delivery in underserved communities. By providing basic healthcare services on-site, HEMA can help to improve access to care, reduce disparities in health outcomes, and improve the overall health and well-being of people in underserved communitie