A flexible and scalable SLAM system with full 3D motion estimation

By Stefan Kohlbrecher, O. von Stryk, Johannes Meyer, Uwe Klingauf. IEEE International Symposium on Safety, Security and Rescue Robotics, 2011

1,091 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/81f21574260d03fbe68ac73bc6a1da2c9645301d

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Citations in the corpus

RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation (2018, methodology): “Hector SLAM does not need external odometry, which can be an advantage when the robot does not have one, but can be a disadvantage when operating in an environment without a lot of geometry constraints, limiting laser scan matching performance.”
Deep reinforcement learning based mobile robot navigation: A review (2021, methodology): “The laser SLAM algorithm directly constructs an obstacle map of the environment based on the dense laser ranging results of algorithms such as GMapping [4] and Hector SLAM [5] .”
Comprehensive Simulation of Quadrotor UAVs Using ROS and Gazebo (2012, methodology): “To demonstrate the applicability for indoor SLAM simulation, we deploy a previously developed SLAM approach [14] on the quadrotor UAV.”
Beyond the Nav-Graph: Vision-and-Language Navigation in Continuous Environments (2020, background): “For example, simultaneous localization and mapping (SLAM) or odometry systems can oﬀer strong localization in appropriate conditions [16,21].”
A2D2: Audi Autonomous Driving Dataset (2020, neutral): “[19, 20, 21].”
LOAM: Lidar Odometry and Mapping in Real-time (2014, methodology): “For localization and mapping, most applications use 2D lidars [11].”
Multiple‐Robot Simultaneous Localization and Mapping: A Review (2016, background): “, 2009), or matching a scan with the most recent map of the environment, for instance (Kohlbrecher et al., 2011).”
Learning to Explore using Active Neural SLAM (2020, methodology): “In order to get an accurate agent pose, we use an Hokuyo UST-10LX Scanning Laser Rangeﬁnder (LiDAR) which is especially very precise in our scenario as we take static readings in 2D (Kohlbrecher et al., 2011).”
Success Weighted by Completion Time: A Dynamics-Aware Evaluation Criteria for Embodied Navigation (2021, methodology): “of the fastest path, we use RRT*-Unicycle on the floorplan generated by the Hector SLAM algorithm [11] using the LiDAR.”
Self-Driving Cars: A Survey (2019, methodology): “There are other algorithms for online grid mapping (Hess et al., 2016; Kohlbrecher et al., 2011).”
Sim2Real Predictivity: Does Evaluation in Simulation Predict Real-World Performance? (2019, methodology): “We run the LIDAR-based Hector SLAM [33] algorithm to provide the location+heading for the GPS+Compass sensor and for computing success and SPL of tests in the lab.”
Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM (2019, methodology): “For the scene of corridor and cafe , a variant of hector mapping [15] is used for map construction and localization.”
A Fully-Autonomous Aerial Robot for Search and Rescue Applications in Indoor Environments using Learning-Based Techniques (2018, methodology): “In this work, localization and mapping capabilities have been integrated by means of a state-of-the-art 2D SLAM algorithm [25], which has been extensively tested for ground robots in Urban Search and Rescue (USAR) missions, and can be also utilized in platforms that exhibit roll/pitch motion by…”
Real-time loop closure in 2D LIDAR SLAM (2016, methodology): “One such approach, which uses Gauss-Newton to find local optima on a linearly interpolated map, is [5].”