TY - GEN
T1 - Extending occupancy grid mapping for dynamic environments
AU - Wessner, Joseph
AU - Utschick, Wolfgang
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/18
Y1 - 2018/10/18
N2 - In this paper, the commonly used filtering technique occupancy grid mapping for static environments is extended for dynamic environments. The proposed method is able to estimate velocities indirectly. We apply a distribution model of the respective state variable to estimate the cell dynamics by means of prediction and update cycle, as known by standard tracking filters. Therefore, we present a straight forward derivation of the prediction and update rule. Furthermore, we validate our approach by simple one dimensional simulations, and show how it can be extended into a two dimensional world, including the resulting consequences, e.g. in terms of memory requirements.
AB - In this paper, the commonly used filtering technique occupancy grid mapping for static environments is extended for dynamic environments. The proposed method is able to estimate velocities indirectly. We apply a distribution model of the respective state variable to estimate the cell dynamics by means of prediction and update cycle, as known by standard tracking filters. Therefore, we present a straight forward derivation of the prediction and update rule. Furthermore, we validate our approach by simple one dimensional simulations, and show how it can be extended into a two dimensional world, including the resulting consequences, e.g. in terms of memory requirements.
UR - http://www.scopus.com/inward/record.url?scp=85056781754&partnerID=8YFLogxK
U2 - 10.1109/IVS.2018.8500362
DO - 10.1109/IVS.2018.8500362
M3 - Conference contribution
AN - SCOPUS:85056781754
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 701
EP - 707
BT - 2018 IEEE Intelligent Vehicles Symposium, IV 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Intelligent Vehicles Symposium, IV 2018
Y2 - 26 September 2018 through 30 September 2018
ER -