Long-Term Autonomy in Forest Environment using Self-Corrective SLAM

  title={Long-Term Autonomy in Forest Environment using Self-Corrective SLAM},
  author={Paavo Nevalainen and Parisa Movahedi and Jorge Pe{\~n}a Queralta and Tomi Westerlund and Jukka Heikkonen},
Vehicles with prolonged autonomous missions have to maintain environment awareness by simultaneous localization and mapping (SLAM). Closed loop correction is substituted by interpolation in rigid body transformation space in order to systematically reduce the accumulated error over different scales. The computation is divided to an edge computed lightweight SLAM and iterative corrections in the cloud environment. Tree locations in the forest environment are sent via a potentially limited… 
Intelligent Robotics and Embedded Systems at the University of Turku
  • Computer Science
  • 2021
The key research directions and recent developments within the TIERS Lab at the Faculty of Technology, University of Turku, Finland are edge computing, autonomous robots and multi-robot systems, and a key focus is on embedding intelligence through lightweight machine learning and dynamic offloading in edge devices and mobile robots.


Navigation and Mapping in Forest Environment Using Sparse Point Clouds
A two-phase on-board process, where tree stem registration produces a sparse point cloud (PC) which is then used for simultaneous location and mapping (SLAM) and a generic SLAM post-processing method is presented, which improves the odometric accuracy with a small additional processing cost.
SLAM-Aided Stem Mapping for Forest Inventory with Small-Footprint Mobile LiDAR
This paper investigates a Simultaneous Localization and Mapping-aided positioning solution with point clouds collected by a small-footprint LiDAR and shows that the positioning accuracy in the selected test field is improved by 38% compared to that of a traditional tactical grade IMU + GNSS positioning system in a mature forest environment and, as a result, the map is able to produce a unambiguous tree distribution map.
Loam livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV
  • Jiarong Lin, Fu Zhang
  • Environmental Science
    2020 IEEE International Conference on Robotics and Automation (ICRA)
  • 2020
This paper presents a robust, real-time LOAM algorithm for LiDARs with small FoV and irregular samplings, and addresses several fundamental challenges arising from such LiDars, and achieves better performance in both precision and efficiency compared to existing baselines.
GNSS/IMU/ODO/LiDAR-SLAM Integrated Navigation System Using IMU/ODO Pre-Integration
The test in the real tunnel case shows that in weak environmental feature areas where the LiDAR-SLAM can barely work, the assistance of the odometer in the pre-integration is critical and can effectually reduce the positioning drift along the forward direction and maintain the SLAM in the short-term.
A Brief Review on Loop Closure Detection with 3D Point Cloud
A summarization on the recent developments of LCD method with 3D data source, especially with3D point cloud input, and classify these methods into three groups, point feature based, segmentation/object based, and learning-based methods.
LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain
  • Tixiao Shan, Brendan Englot
  • Computer Science
    2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • 2018
A lightweight and ground-optimized lidar odometry and mapping method, LeGO-LOAM, for realtime six degree-of-freedom pose estimation with ground vehicles and integrated into a SLAM framework to eliminate the pose estimation error caused by drift is integrated.
Probabilistic data association for semantic SLAM
This paper forms an optimization problem over sensor states and semantic landmark positions that integrates metric information, semantic information, and data associations, and decomposes it into two interconnected problems: an estimation of discrete data association and landmark class probabilities, and a continuous optimization over the metric states.