Indoor Inertial Waypoint Navigation for the Blind

Indoor navigation technology is needed to support seamless mobility for the visually impaired. This paper describes the construction and evaluation of an inertial dead reckoning navigation system that provides real-time auditory guidance along mapped routes. Inertial dead reckoning is a navigation technique coupling step counting together with heading estimation to compute changes in position at each step. The research described here outlines the development and evaluation of a novel navigation system that utilizes information from the mapped route to limit the problematic error accumulation inherent in traditional dead reckoning approaches. The prototype system consists of a wireless inertial sensor unit, placed at the users’ hip, which streams readings to a smartphone processing a navigation algorithm. Pilot human trials were conducted assessing system efficacy by studying route-following performance with blind and sighted subjects using the navigation system with real-time guidance, versus offline verbal directions

Finding Your Way Back: Comparing Path Odometry Algorithms for Assisted Return.

We present a comparative analysis of inertial-based odometry algorithms for the purpose of assisted return. An assisted return system facilitates backtracking of a path previously taken, and can be particularly useful for blind pedestrians. We present a new algorithm for path matching, and test it in simulated assisted return tasks with data from WeAllWalk, the only existing data set with inertial data recorded from blind walkers. We consider two odometry systems, one based on deep learning (RoNIN), and the second based on robust turn detection and step counting. Our results show that the best path matching results are obtained using the turns/steps odometry system

Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

Evaluating Indoor Positioning Systems in a Shopping Mall: The Lessons Learned From the IPIN 2018 Competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75 th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

Assessment of Audio Interfaces for use in Smartphone Based Spatial Learning Systems for the Blind

Recent advancements in the field of indoor positioning and mobile computing promise development of smart phone based indoor navigation systems. Currently, the preliminary implementations of such systems only use visual interfaces—meaning that they are inaccessible to blind and low vision users. According to the World Health Organization, about 39 million people in the world are blind. This necessitates the need for development and evaluation of non-visual interfaces for indoor navigation systems that support safe and efficient spatial learning and navigation behavior. This thesis research has empirically evaluated several different approaches through which spatial information about the environment can be conveyed through audio. In the first experiment, blindfolded participants standing at an origin in a lab learned the distance and azimuth of target objects that were specified by four audio modes. The first three modes were perceptual interfaces and did not require cognitive mediation on the part of the user. The fourth mode was a non-perceptual mode where object descriptions were given via spatial language using clockface angles. After learning the targets through the four modes, the participants spatially updated the position of the targets and localized them by walking to each of them from two indirect waypoints. The results also indicate hand motion triggered mode to be better than the head motion triggered mode and comparable to auditory snapshot. In the second experiment, blindfolded participants learned target object arrays with two spatial audio modes and a visual mode. In the first mode, head tracking was enabled, whereas in the second mode hand tracking was enabled. In the third mode, serving as a control, the participants were allowed to learn the targets visually. We again compared spatial updating performance with these modes and found no significant performance differences between modes. These results indicate that we can develop 3D audio interfaces on sensor rich off the shelf smartphone devices, without the need of expensive head tracking hardware. Finally, a third study, evaluated room layout learning performance by blindfolded participants with an android smartphone. Three perceptual and one non-perceptual mode were tested for cognitive map development. As expected the perceptual interfaces performed significantly better than the non-perceptual language based mode in an allocentric pointing judgment and in overall subjective rating. In sum, the perceptual interfaces led to better spatial learning performance and higher user ratings. Also there is no significant difference in a cognitive map developed through spatial audio based on tracking user’s head or hand. These results have important implications as they support development of accessible perceptually driven interfaces for smartphones

Intelligent Banal type INS based Wassily chair (INSW)

oai:ojs2.ijnpme.org:article/2The navigation of physically impaired requires a continuous positioning with certain accuracy in their environments. This paper proposes an automated wheel chair developed for the indoor navigation using Inertial Navigation System (INS) for the physically blight persons. The Wassily chairs are the mobile chairs that facilitate the movement of the user in pre-functioned places. This is an intelligent vehicle which has all the feasibility for the usage of the physically impaired. This mobile chair replaces the traditional gear system with the keypad system to reach the destined places and to locate the things in the destined places. This makes the vehicle smart and user-friendly augmenting the viability to carry out their day to day activities.  It has an additional feature, the automatic airbag system which provides hip bone protections

WayFAST: Navigation with Predictive Traversability in the Field

We present a self-supervised approach for learning to predict traversable paths for wheeled mobile robots that require good traction to navigate. Our algorithm, termed WayFAST (Waypoint Free Autonomous Systems for Traversability), uses RGB and depth data, along with navigation experience, to autonomously generate traversable paths in outdoor unstructured environments. Our key inspiration is that traction can be estimated for rolling robots using kinodynamic models. Using traction estimates provided by an online receding horizon estimator, we are able to train a traversability prediction neural network in a self-supervised manner, without requiring heuristics utilized by previous methods. We demonstrate the effectiveness of WayFAST through extensive field testing in varying environments, ranging from sandy dry beaches to forest canopies and snow covered grass fields. Our results clearly demonstrate that WayFAST can learn to avoid geometric obstacles as well as untraversable terrain, such as snow, which would be difficult to avoid with sensors that provide only geometric data, such as LiDAR. Furthermore, we show that our training pipeline based on online traction estimates is more data-efficient than other heuristic-based methods.Comment: Project website with code and videos: https://mateusgasparino.com/wayfast-traversability-navigation/ Published in the IEEE Robotics and Automation Letters (RA-L, 2022) Accepted for presentation in the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022