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.

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Certain species of ants can carry out tasks in dense work spaces while maintaining their ability to accurately manipulate heavy loads, and these advantages are of interest to the robotics community. We consider a robotic swarm of N ≥ 6 agents that assumes the task of moving a load through a cluttered space. This forces the swarm to carefully manipulate the orientation of the load, while transporting it to its destination point. We model this scenario as a 6-PPSS (Prismatic-Prismatic-Spherical-Spherical) redundant mobile platform, having six degrees of freedom. As with insects, the multitude of agents enables sharing the burden of the load in the case that one or more agents are blocked by an obstacle. We model this by a semi-algebraic set of constraints on the distances between the agents and the load. We apply an Extended Kalman Filter routine, in order to estimate their relative locations. We show how the estimation-error is reduced when position-information is shared among the agents. These estimations are then used to calculate the full configuration and investigate the effect of position estimation error on the platform heading error. We show how motion planning can then be calculated in the model's full configuration space and demonstrate this with a distributed control scheme. To reduce the search time, we introduce a variant of the crawling probabilistic road map motion planning algorithm under a set of kinematic constraints and work-space obstacles. Finally, we exemplify our algorithms on several simulated scenarios. INDEX TERMS Swarm, load, extended Kalman filter, parallel platform, crawling probabilistic road map.

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