Free-Space Features: Global Localization in 2D Laser SLAM Using Distance Function Maps

@article{Millane2019FreeSpaceFG,
  title={Free-Space Features: Global Localization in 2D Laser SLAM Using Distance Function Maps},
  author={Alexander Millane and Helen Oleynikova and Juan I. Nieto and Roland Y. Siegwart and C{\'e}sar Cadena},
  journal={2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2019},
  pages={1271-1277}
}
In many applications, maintaining a consistent map of the environment is key to enabling robotic platforms to perform higher-level decision making. Detection of already visited locations is one of the primary ways in which map consistency is maintained, especially in situations where external positioning systems are unavailable or unreliable. Mapping in 2D is an important field in robotics, largely due to the fact that man-made environments such as warehouses and homes, where robots are… 

Figures and Tables from this paper

Freetures: Localization in Signed Distance Function Maps

A system for geometry-based localization that extracts features directly from an implicit surface representation: the Signed Distance Function (SDF), which varies continuously through space, which allows the proposed system to extract and utilize features describing both surfaces and free-space.

High-precision and robust localization system for mobile robots in complex and large-scale indoor scenes

Experimental results show that the proposed high-precision and robust localization system can achieve robust robot localization and real-time detection of the current localization quality in indoor scenes and industrial environment.

Reliable Monte Carlo Localization for Mobile Robots

  • Naoki Akai
  • Computer Science
    Journal of Field Robotics
  • 2023
This paper presents a novel localization framework that enables robust localization, reliability estimation, and quick re-localization, simultaneously, and can seamlessly integrate a global localization method via importance sampling.

Learning Deep SDF Maps Online for Robot Navigation and Exploration

—We propose an algorithm to (i) learn online a deep signed distance function (SDF) with a LiDAR-equipped robot to represent the 3D environment geometry, and (ii) plan collision-free trajectories

Extracting Statistical Signatures of Geometry and Structure in 2D Occupancy Grid Maps for Global Localization

Experiments demonstrate that the proposed method outperforms the other state-of-the-art image-based methods by examining precision-recall curve especially when occupancy noise added to the submap is progressively increased.

References

SHOWING 1-10 OF 27 REFERENCES

Keypoint design and evaluation for place recognition in 2D lidar maps

A new approach to global self-localization with laser range scans in unstructured environments

  • A. Walthelm
  • Computer Science
    Intelligent Vehicle Symposium, 2002. IEEE
  • 2002
A new algorithm which determines the position and orientation of the robot by matching an 180/spl deg/ laser range scan to a sensor based 2D world model deduced from previously recorded laser range scans is presented.

Simultaneous Localization and Map Building in Large-Scale Cyclic Environments Using the Atlas Framework

Atlas is described, a hybrid metrical/topological approach to simultaneous localization and mapping (SLAM) that achieves efficient mapping of large-scale environments through an efficient map-matching algorithm coupled with a cycle verification step.

2D-SDF-SLAM: A signed distance function based SLAM frontend for laser scanners

A novel approach to simultaneous localization and mapping for robots equipped with a 2D laser scanner that operates on 2D maps represented as a signed distance function (SDF) is introduced.

Efficient grid-based spatial representations for robot navigation in dynamic environments

Multi-Robot Localization and Mapping Based on Signed Distance Functions

A 2D Simultaneous Localization and Mapping approach applicable to multiple mobile robots using data of 2D LIDAR sensors to build a dynamic representation based on Signed Distance Functions.

Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age

What is now the de-facto standard formulation for SLAM is presented, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers.

Voxblox: Incremental 3D Euclidean Signed Distance Fields for on-board MAV planning

This work proposes a method to incrementally build ESDFs from Truncated Signed Distance Fields (TSDFs), a common implicit surface representation used in computer graphics and vision, and shows that it can build TSDFs faster than Octomaps, and that it is more accurate than occupancy maps.

An object-based semantic world model for long-term change detection and semantic querying

  • Julian MasonB. Marthi
  • Computer Science
    2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
  • 2012
This work describes and experimentally verify a semantic querying system aboard a mobile robot equipped with a Microsoft Kinect RGB-D sensor, which allows the system to operate in large, dynamic, and uncon-strained environments, where modeling every object that occurs or might occur is impractical.

Geometrical FLIRT phrases for large scale place recognition in 2D range data

Geometrical FLIRT phrases (GFPs) are introduced as a novel retrieval method for very efficient and precise place recognition and largely outperform state-of-the-art approaches in 2D range-based place recognition in terms of efficiency and recall.