TY - JOUR
T1 - Elevation moment of inertia
T2 - A new feature for monte Carlo localization in outdoor environment with elevation map
AU - Kwon, Tae Bum
AU - Song, Jae Bok
AU - Joo, Sang Hyun
PY - 2010/5
Y1 - 2010/5
N2 - The elevation map is one of themost popular maps for outdoor navigation.We propose the elevation moment of inertia (EMOI), which represents the distribution of elevation around a robot in an elevation map, for use in the matching of elevation maps. Using this feature, outdoor localization can be performed with an elevation map without external positioning systems. In this research, the Monte Carlo localization (MCL) method is used for outdoor localization, and the conventional method is based on range matching, which compares range sensor data with the range data predicted from an elevation map. Our proposed method is based on EMOI matching. The EMOI around a robot is compared with the EMOIs for all cells of the pregiven reference elevation map to find a robot pose with respect to the reference map. MCL based on EMOI matching is very fast, although its accuracy is slightly lower than that of conventional range matching. To deal with the disadvantage of EMOI matching, an adaptive switching scheme between EMOI matching and range matching was also proposed. Various outdoor experiments indicated that the proposed EMOI significantly reduced the convergence time of MCL. Therefore, the proposed feature is considered to be useful when an elevation map is used for outdoor localization.
AB - The elevation map is one of themost popular maps for outdoor navigation.We propose the elevation moment of inertia (EMOI), which represents the distribution of elevation around a robot in an elevation map, for use in the matching of elevation maps. Using this feature, outdoor localization can be performed with an elevation map without external positioning systems. In this research, the Monte Carlo localization (MCL) method is used for outdoor localization, and the conventional method is based on range matching, which compares range sensor data with the range data predicted from an elevation map. Our proposed method is based on EMOI matching. The EMOI around a robot is compared with the EMOIs for all cells of the pregiven reference elevation map to find a robot pose with respect to the reference map. MCL based on EMOI matching is very fast, although its accuracy is slightly lower than that of conventional range matching. To deal with the disadvantage of EMOI matching, an adaptive switching scheme between EMOI matching and range matching was also proposed. Various outdoor experiments indicated that the proposed EMOI significantly reduced the convergence time of MCL. Therefore, the proposed feature is considered to be useful when an elevation map is used for outdoor localization.
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U2 - 10.1002/rob.20338
DO - 10.1002/rob.20338
M3 - Article
AN - SCOPUS:77952180744
VL - 27
SP - 371
EP - 386
JO - Journal of Field Robotics
JF - Journal of Field Robotics
SN - 1556-4959
IS - 3
ER -