A Multisensor Factor-Graph SLAM Framework for Steep Slope Vineyards

Steep slope vineyards pose specific challenges for autonomous robot navigation, therefore requiring accurate, robust and scalable localization and mapping solutions for such goal. In addition, due to the unevenness of the terrain, the identification of traversable zones is crucial for a safe operation, thus requiring a dense scene representation that captures these details. For such reasons, a novel SLAM architecture is presented in this work, characterized by a multi-sensor based dual factor-graph framework that integrates in real time wheel odometry, IMU, LIDAR and GNSS measurements, as well as heading and attitude data, generating a dense 3D map in point cloud format. The proposed system was tested with datasets obtained from a real robot navigating in vineyards with different levels of steepness, and benchmarked with state-of-the-art 3D LIDAR SLAM techniques. The presented results demonstrate superior performance over the compared methods, while maintaining overall map consistency and accuracy when matched with a reference model.