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

Online Learning and Planning for Crowd-aware Service Robot Navigation

Mobile service robots are increasingly used in indoor environments (e.g., shopping malls or museums) among large crowds of people. To efficiently navigate in these environments, such a robot should be able to exhibit a variety of behaviors. It should avoid crowded areas, and not oppose the flow of the crowd. It should be able to identify and avoid specific crowds that result in additional delays (e.g., children in a particular area might slow down the robot). and to seek out a crowd if its task requires it to interact with as many people as possible. These behaviors require the ability to learn and model crowd behavior in an environment. Earlier work used a dataset of paths navigated by people to solve this problem. That approach is expensive, risks privacy violations, and can become outdated as the environment evolves. To overcome these drawbacks, this thesis proposes a new approach where the robot learns models of crowd behavior online and relies only on local onboard sensors. This work develops and tests multiple planners that leverage these models in simulated environments and demonstrate statistically significant improvements in performance. The work reported here is applicable not only to navigation to target locations, but also to a variety of other services

From Perception to Navigation in Environments with Persons: An Indoor Evaluation of the State of the Art

Research in the field of social robotics is allowing service robots to operate in environments with people. In the aim of realizing the vision of humans and robots coexisting in the same environment, several solutions have been proposed to (1) perceive persons and objects in the immediate environment; (2) predict the movements of humans; as well as (3) plan the navigation in agreement with socially accepted rules. In this work, we discuss the different aspects related to social navigation in the context of our experience in an indoor environment. We describe state-of-the-art approaches and experiment with existing methods to analyze their performance in practice. From this study, we gather first-hand insights into the limitations of current solutions and identify possible research directions to address the open challenges. In particular, this paper focuses on topics related to perception at the hardware and application levels, including 2D and 3D sensors, geometric and mainly semantic mapping, the prediction of people trajectories (physics-, pattern- and planning-based), and social navigation (reactive and predictive) in indoor environments