In the project entitled Sidewalk Environment Detection System for Assistive NavigaTION (hereinafter referred to as SENSATION), our research team is meticulously advancing the development of the components of SENSATION. The primary objective of this venture is to enhance the mobility capabilities of blind or visually impaired persons (BVIPs) by ensuring safer and more efficient navigation on pedestrian pathways.
For the implementation phase, a specialized prototype was engineered: a chest-bag equipped with an NVIDIA Jetson Nano, serving as the core computational unit. This device integrates a several sensors including, but not limited to, tactile feedback mechanisms (vibration motors) for direction indication, optical sensors (webcam) for environmental data acquisition, wireless communication modules (Wi-Fi antenna) for internet connectivity, and geospatial positioning units (GPS sensors) for real-time location tracking.
Despite the promising preliminary design of the prototype, several technical challenges remain that demand investigation. These challenges are described as follows:
Sidewalk segmentation for direction estimation
To determine the location of a BVIP on the pedestrian pathway, it is imperative for our algorithms to achieve optimal segmentation of the sidewalk. To facilitate this, we continuously refine our proprietary dataset tailored to sidewalk segmentation. We are also exploring a variety of Deep Learning methodologies to enhance the accuracy of this segmentation. The primary objective in this topic is to refine our sidewalk segmentation pipeline and to comprehensively evaluate its performance using metrics such as Mean Intersection over Union (IOU), and precision metrics for both sidewalks and roads. Additionally, we employ Active Learning techniques to further analyze our dataset, aiming to gain a deeper insight into its characteristics.
Distance estimation for obstacles avoidance
To convey information to a BVIP regarding the presence of an impediment on the pedestrian pathway, obstacles such as bicycles, e-scooters, or automobiles must initially be identified via image segmentation techniques. After this identification, it is crucial to determine the distance from these detected objects. The SENSATION system employs a monocular camera to capture the surrounding environmental details of the pathway. In this domain of research, we are studying various algorithms tailored for depth estimation to determine the proximity to these impediments. The calculated distances are then conveyed to the BVIP through either tactile or auditory feedback mechanisms. A prominent challenge in this work lies in achieving precise distance measurements, particularly given the constraints of solely utilizing information from a monocular camera.
Drift correction to improve orientation of a BVIP
While navigating pedestrian pathways, it is occasionally observed that a BVIP may lose orientation with respect to the sidewalk. Addressing this, it is essential to devise a detection system capable of promptly identifying a BVIP’s deviation from the intended sidewalk. For the detection of such drifts, we employ Deep Learning algorithms that leverage optical flow or depth maps. The primary objective in this topic is to conceptualize and develop a drift correction mechanism utilizing either optical flow or depth maps to enhance a BVIP’s sidewalk orientation.
Environmental information by image captioning
To augment a BVIP’s comprehension of their surrounding environment, descriptive captions derived from environmental observations are beneficial. Examples of such captions include: “Traffic light located on your right,” “Staircase descending with a total of 5 steps,” and “Vehicle parked obstructing the sidewalk.”
In this topic, we are examining Deep Learning algorithms that possess the capacity to generate such descriptive annotations. Concurrently, we are refining our caption generation pipeline to ascertain the spectrum of captions that can be formulated to enhance the mobility and spatial understanding of a BVIP.
If you are interested in one of the above topics, please send your request to: hakan.calim@fau.de
For the development of the solutions, it will be beneficial to have experience with implementing neural networks in python with Pytorch or Tensorflow.