Flying with Cartographer: Adapting the Cartographer 3D Graph SLAM Stack for UAV Navigation

TL;DR

This paper adapts the Cartographer 3D SLAM stack for UAV navigation by integrating pose estimation with GPS and LiDAR data, and utilizing OctoMap for environment mapping and obstacle avoidance.

Contribution

It introduces application-specific modifications to Cartographer SLAM for UAVs, including pose smoothing and integration with OctoMap for navigation.

Findings

Effective UAV pose estimation using fused LiDAR, IMU, and GPS data.

Successful implementation of environment mapping with OctoMap for navigation.

Enhanced obstacle avoidance capabilities in UAVs using the adapted SLAM stack.

Abstract

This paper describes an application of the Cartographer graph SLAM stack as a pose sensor in a UAV feedback control loop, with certain application-specific changes in the SLAM stack such as smoothing of the optimized pose. Pose estimation is performed by fusing 3D LiDAR/IMU-based proprioception with GPS position measurements by means of pose graph optimisation. Moreover, partial environment maps built from the LiDAR data (submaps) within the Cartographer SLAM stack are marshalled into OctoMap, an Octree-based voxel map implementation. The OctoMap is further used for navigation tasks such as path planning and obstacle avoidance.