Multi-Level Surface Maps for Outdoor Terrain Mapping and Loop Closing

By Rudolph Triebel, Patrick Pfaff, Wolfram Burgard. 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2006

404 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

OctoMap : A Probabilistic , Flexible , and Compact 3 D Map Representation for Robotic Systems (2010, background): “Figure 1 depicts a tree observed in 3D laser range scans and modeled in three commonly used representations, namely point clouds, elevation maps [7], and multi-level surface maps [19].”
Detection, classification and tracking of moving objects in a 3D environment (2012, methodology): “It implements an efficient data structure using a 2D projection of the 3D space for outdoor mapping.”
A Tutorial on Graph-Based SLAM (2010, background): “In contrast, dense representations [33], [12], [9] are usually used in conjunction with range sensors.”
Fast LIDAR localization using multiresolution Gaussian mixture maps (2015, methodology): “In doing so, we integrate odometry, GPS, and scanmatching constraints to build a self-consistent pose-graph.”
OctoMap: an efficient probabilistic 3D mapping framework based on octrees (2013, background): “The strict assumption of a single surface can be relaxed by allowing multiple surfaces per cell (Triebel et al., 2006; Pfaff et al., 2007), or by using classes of cells which correspond to different types of structures (Gutmann et al.”
Globally consistent 3D mapping with scan matching (2008, background): “Triebel et al. apply this approach to their multi-level surface maps [36].”