EMap: Real-Time Terrain Estimation

TY - GEN

T1 - EMap

T2 - 23rd Annual Conference Towards Autonomous Robotic Systems, TAROS 2022

AU - Lock, Jacobus C.

AU - Camara, Fanta

AU - Fox, Charles

N1 - Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2022/9/1

Y1 - 2022/9/1

N2 - Terrain mapping has a many use cases in both land surveyance and autonomous vehicles. Popular methods generate occupancy maps over 3D space, which are sub-optimal in outdoor scenarios with large, clear spaces where gaps in LiDAR readings are common. A terrain can instead be modelled as a height map over 2D space which can iteratively be updated with incoming LiDAR data, which simplifies computation and allows missing points to be estimated based on the current terrain estimate. The latter point is of particular interest, since it can reduce the data collection effort required (and its associated costs) and current options are not suitable to real-time operation. In this work, we introduce a new method that is capable of performing such terrain mapping and inferencing tasks in real-time. We evaluate it with a set of mapping scenarios and show it is capable of generating maps with higher accuracy than an OctoMap-based method.

AB - Terrain mapping has a many use cases in both land surveyance and autonomous vehicles. Popular methods generate occupancy maps over 3D space, which are sub-optimal in outdoor scenarios with large, clear spaces where gaps in LiDAR readings are common. A terrain can instead be modelled as a height map over 2D space which can iteratively be updated with incoming LiDAR data, which simplifies computation and allows missing points to be estimated based on the current terrain estimate. The latter point is of particular interest, since it can reduce the data collection effort required (and its associated costs) and current options are not suitable to real-time operation. In this work, we introduce a new method that is capable of performing such terrain mapping and inferencing tasks in real-time. We evaluate it with a set of mapping scenarios and show it is capable of generating maps with higher accuracy than an OctoMap-based method.

UR - http://www.scopus.com/inward/record.url?scp=85138011379&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-15908-4_10

DO - 10.1007/978-3-031-15908-4_10

M3 - Conference contribution

AN - SCOPUS:85138011379

SN - 9783031159077

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 114

EP - 127

BT - Towards Autonomous Robotic Systems - 23rd Annual Conference, TAROS 2022, Proceedings

A2 - Pacheco-Gutierrez, Salvador

A2 - Cryer, Alice

A2 - Caliskanelli, Ipek

A2 - Tugal, Harun

A2 - Skilton, Robert

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 7 September 2022 through 9 September 2022

ER -