Indoor Coverage Path Planning: Survey, Implementation, Analysis

Bormann, Richard; Jordan, Florian; Hampp, Joshua; Hägele, Martin

doi:10.1109/icra.2018.8460566

Cited by 72 publications

(37 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…al. [2]. Choset [1] related the problem to variant of TSP, covering salesman problem, where instead of visiting all the points individually in a region, an agent must visit a single point in neighbourhood of the region from where the agent's sensor can cover the entire neighbourhood of the point.…”

Section: Related Workmentioning

confidence: 99%

Coverage Path Planning Techniques for Inspection of Disjoint Regions With Precedence Provision

et al. 2021

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

Coverage Path Planning Techniques for Inspection of Disjoint Regions With Precedence Provision

et al. 2021

View full text Add to dashboard Cite

“…More concretely, in the world of path planning, there exists coverage path planning (CPP) [9] and map exploration (ME). CPP algorithms generate structured paths achieving coverage of around 95%, whereas ME algorithms use Probabilistic Road-Maps [30] or Rapidly-searching Random Trees (RRT) [39] to randomly explore the map to find a route between two points.…”

Section: Related Workmentioning

confidence: 99%

Deep learning based wireless localization for indoor navigation

Ayyalasomayajula

Arun

et al. 2020

Proceedings of the 26th Annual International Conference on Mobile Computing and Networking

138

View full text Add to dashboard Cite

Location services, fundamentally, rely on two components: a mapping system and a positioning system. The mapping system provides the physical map of the space, and the positioning system identifies the position within the map. Outdoor location services have thrived over the last couple of decades because of well-established platforms for both these components (e.g. Google Maps for mapping, and GPS for positioning). In contrast, indoor location services haven't caught up because of the lack of reliable mapping and positioning frameworks. Wi-Fi positioning lacks maps and is also prone to environmental errors. In this paper, we present DLoc, a Deep Learning based wireless localization algorithm that can overcome traditional limitations of RF-based localization approaches (like multipath, occlusions, etc.). We augment DLoc with an automated mapping platform, MapFind. MapFind constructs location-tagged maps of the environment and generates training data for DLoc. Together, they allow off-the-shelf Wi-Fi devices like smartphones to access a map of the environment and to estimate their position with respect to that map. During our evaluation, MapFind has collected location estimates of over 105 thousand points under 8 different scenarios with varying furniture positions and people motion across two different spaces covering 2000 sq. Ft. DLoc outperforms stateof-the-art methods in Wi-Fi-based localization by 80% (median & 90 th percentile) across the two different spaces. CCS CONCEPTS • Networks → Location based services; • Computing methodologies → Robotic planning; Supervised learning; • Information systems → Sensor networks.

show abstract

“…This approach never visits grid cells twice and terminates when all grid cells have been covered. Other wellknown grid-based CPP methods are the Grid-based Travelling Salesman Problem (Grid-based TSP) algorithm [30] and Graph theory-based technique [31]. The planner decomposes the map into grid cells, and the solution to CPP is determined by finding the shortest path that visits all the grid cells as TSP.…”

Section: Related Workmentioning

confidence: 99%