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DeWitt Latimer IV

dblp:02/5268 · DBLP profile ↗
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3ranked-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 · 3 · 1 first-authorSystems, architecture and hardware · 2 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1

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
2 papers
Robot navigation and mapping · 53% Motion planning and robot control · 26% Multi-agent systems · 20%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Smart cities and intelligent transportation · 100%

Topics — the 5 heaviest of 6, 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
Robotics › Motion planning and robot control › path planning › coverage path planning
multi-robot coverage
0.012002
Towards Sensor Based Coverage with Robot Teams · ICRA 2002
Knowledge, reasoning and agents › Multi-agent systems
multi-robot systems
0.012002
Towards Sensor Based Coverage with Robot Teams · ICRA 2002
Robotics › Motion planning and robot control › path planning
coverage path planning
0.012002
Towards Sensor Based Coverage with Robot Teams · ICRA 2002

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

path generation · 0.1clustering of information goals · 0.1lidar sensing · 0.0LIDAR sensing · 0.0semi-decentralized coordination · 0.0cell-based coverage · 0.0boustrophedon decomposition · 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
ICRA2
2002 Towards Sensor Based Coverage with Robot Teams
abstract
We introduce an algorithm to cover an unknown space with a homogeneous team of circular mobile robots. Our approach is based on a single robot coverage algorithm, a boustrophedon approach, which divides the target two-dimensional space into regions called cells, each of which can be covered with simple back and forth motions. Single robot coverage is then achieved by ensuring that the robot visits each cell. The new multi-robot coverage algorithm uses the same planar cell-based approach as the single robot approach, but also prescribes the methods by which multiple robots cover a cell, teams are allocated among cells, and sub-teams of robots share information in a minimalistic manner. The advantage of this method is that planning occurs in a two dimensional configuration space for a team of n robots, bypassing the need to plan in a 2n dimensional configuration space. The approach is semi-decentralized: robot teams cover the space independent of each other, but robots within a team communicate state and share information.
DeWitt Latimer IV, Siddhartha S. Srinivasa, Vincent Lee-Shue, Samuel Sonne, Howie Choset, Aaron P. Hurst
ICRA1
1998 How effective is unsupervised data collection for children's speech recognition?
Gregory Aist, Peggy Chan, Xuedong Huang 0001, Rebecca Kennedy, DeWitt Latimer IV, Jack Mostow
ICSLP6