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Edward Latimer

dblp:10/4217 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2004
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Robot navigation and mapping · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Smart cities and intelligent transportation · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping › sensor planning
sensor placement
0.012004
Sensor Space Planning with Applications to Construction Environments · ICRA 2004
Robotics › Robot navigation and mapping
sensor planning
0.012004
Sensor Space Planning with Applications to Construction Environments · ICRA 2004

Methods — techniques the papers use, named apart from their topics

path generation · 0.1clustering of information goals · 0.1lidar sensing · 0.0LIDAR sensing · 0.0
YearPublicationVenuePosition
2004 Sensor Space Planning with Applications to Construction Environments
abstract
Outlined is a new approach to sensor space planning and its application to the construction industry. The software planning tool described here generates sensor placements automatically for use in assessing deviations in construction environments. The first step is to separate construction information goals into clusters, simplifying the planning space in order to reduce computational complexity. For each cluster, the planner generates the space of potential sensor placements for a set of information goals and selects a minimal set of subspaces to take advantage of views that can achieve multiple goals simultaneously. Sensing locations are chosen that maximize the probability of achieving each goal and a path is generated to minimize the transit cost between the various sensing locations within each cluster. Finally, paths are generated that minimize the transit cost between clusters. This method is demonstrated on a desktop computer and shown to support LIDAR information goal sensor planning within a construction site.
Edward Latimer, DeWitt Latimer IV, Rajiv Saxena, Catherine Lyons, L. Michaux-Smith, Scott Thayer
ICRA1