# SkiMap: An Efficient Mapping Framework for Robot Navigation

## TL;DR

SkiMap introduces a multi-level, efficient mapping framework for robot navigation that supports real-time updates and diverse map representations using a novel Tree of SkipLists data structure.

## Contribution

The paper presents a new mapping framework with a Tree of SkipLists that improves time efficiency and supports real-time map updates for robot navigation.

## Key findings

- Faster than Octree-based methods in time efficiency
- Maintains similar memory footprint for large maps
- Supports real-time erosion and re-integration of measurements

## Abstract

We present a novel mapping framework for robot navigation which features a multi-level querying system capable to obtain rapidly representations as diverse as a 3D voxel grid, a 2.5D height map and a 2D occupancy grid. These are inherently embedded into a memory and time efficient core data structure organized as a Tree of SkipLists. Compared to the well-known Octree representation, our approach exhibits a better time efficiency, thanks to its simple and highly parallelizable computational structure, and a similar memory footprint when mapping large workspaces. Peculiarly within the realm of mapping for robot navigation, our framework supports realtime erosion and re-integration of measurements upon reception of optimized poses from the sensor tracker, so as to improve continuously the accuracy of the map.