TY - GEN
T1 - Cooperative swarm localization and mapping with inter-agent ranging
AU - Lee, Young Hee
AU - Zhu, Chen
AU - Giorgi, Gabriele
AU - Gunther, Christoph
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - Compared to a single robot, a swarm system can conduct a given task in a shorter time, and it is more robust to system failures of each agent. To successfully execute cooperative missions with multiple agents, accurate relative positioning is important. If global positioning (e.g. with a GNSS-based positioning) is available, we can easily compute relative positions. In environments where a global positioning system is unreliable or unavailable, visual odometry can be applied for estimating each agent's egomotion, by exploiting onboard cameras. Using these self-localization results, relative positions between agents can be estimated, once the relative geometry between agents is initialized. However, since visual odometry is a dead-reckoning process, the estimation errors accumulate inherently without bounds. We propose a cooperative localization method using visual odometry and inter-agent range measurements. Using the proposed method, we can reduce the drifts in position estimates with very modest requirements on the communication channel between agents.
AB - Compared to a single robot, a swarm system can conduct a given task in a shorter time, and it is more robust to system failures of each agent. To successfully execute cooperative missions with multiple agents, accurate relative positioning is important. If global positioning (e.g. with a GNSS-based positioning) is available, we can easily compute relative positions. In environments where a global positioning system is unreliable or unavailable, visual odometry can be applied for estimating each agent's egomotion, by exploiting onboard cameras. Using these self-localization results, relative positions between agents can be estimated, once the relative geometry between agents is initialized. However, since visual odometry is a dead-reckoning process, the estimation errors accumulate inherently without bounds. We propose a cooperative localization method using visual odometry and inter-agent range measurements. Using the proposed method, we can reduce the drifts in position estimates with very modest requirements on the communication channel between agents.
UR - http://www.scopus.com/inward/record.url?scp=85087067554&partnerID=8YFLogxK
U2 - 10.1109/PLANS46316.2020.9110227
DO - 10.1109/PLANS46316.2020.9110227
M3 - Conference contribution
AN - SCOPUS:85087067554
T3 - 2020 IEEE/ION Position, Location and Navigation Symposium, PLANS 2020
SP - 353
EP - 359
BT - 2020 IEEE/ION Position, Location and Navigation Symposium, PLANS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE/ION Position, Location and Navigation Symposium, PLANS 2020
Y2 - 20 April 2020 through 23 April 2020
ER -