Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters

By Giorgio Grisetti, Cyrill Stachniss, Wolfram Burgard. IEEE Transactions on robotics, 2007

2,555 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/eac11872f8a29a18058bf61dd04929e1ac29d258

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Citations in the corpus

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].”
Autonomous flight in unstructured and unknown indoor environments (2009, methodology): “The motion model for the particles in the GMapping algorithm was based on a standard motion model for ground robots with wheel odometry.”
On behalf of: Multimedia Archives (2012, positive): “First of all, an improved version of Olson's SGD (Olson et al., 2006) as implemented in the popular TORO library (Grisetti et al., 2007c) is used.”
Evaluating Egomotion and Structure-from-Motion Approaches Using the TUM RGB-D Benchmark (2012, background): “Different sensor modalities have been explored in the past, including 2D laser scanners [25], [26], 3D scanners [27]– [29], monocular cameras [17], [22]–[24], [30]–[32], stereo”
A benchmark for the evaluation of RGB-D SLAM systems (2012, background): “Different sensor modalities have been explored in the past, including 2D laser scanners [17], [18], 3D scanners [19]– [21], monocular cameras [9], [14]–[16], [22]–[24], stereo systems [25], [26] and recently RGB-D sensors such as the Microsoft Kinect [2]–[4].”
Semantic mapping for mobile robotics tasks: A survey (2015, background): “The representative works described in [14], [17], [18] and [19] prove the necessity for an accurate representation of the robot's surroundings as well as the development of efficient mapping methods.”
RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation (2018, methodology): “Let us start with the following lidar approaches: (cid:129) GMapping (Grisetti, Stachniss, & Burgard, 2007) and TinySLAM (Steux & El Hamzaoui, 2010) are two approaches that use a particle filer to estimate the robot trajectory.”
A Random-Finite-Set Approach to Bayesian SLAM (2011, methodology): “As such, grid-map-estimation errors can be readily defined via Euclidean, mean squared, or sum of squared error metrics [15].”
Active localization with dynamic obstacles (2016, methodology): “Before the experiments, the TurtleBot 2 with the laser range ﬁnder was manually driven (with no dynamic obstacles present) to produce an accurate map of the environment using the ROS gmapping package [17].”
Multiple‐Robot Simultaneous Localization and Mapping: A Review (2016, methodology): “0 for dense scanning laser, such as improved grid mapping (Grisetti et al., 2007), FastSLAM1.”
iSAM: Incremental Smoothing and Mapping (2008, methodology): “We evaluate pose-only iSAM on simulated as well as real-world datasets, assuming known data association, as the generation of pose constraints by scan-matching has been well studied and good algorithms are available [22], [23].”
Sub-meter indoor localization in unmodified environments with inexpensive sensors (2010, methodology): “D map of the environment and correct the odometry of the robot, a horizontal LIDAR is used with the GMapping SLAM system, an efficient open-source implementation of gridbased FastSLAM [25].”
Towards a benchmark for RGB-D SLAM evaluation (2011, background): “Different sensor modalities have been explored in the past, including 2D laser scanners [12], [3], 3D scanners [14], [16], monocular cameras [13], [7], [9], [19], [20] and stereo systems [8].”
High-accuracy 3D sensing for mobile manipulation: Improving object detection and door opening (2009, methodology): “For the intensity and gradient channels we normalize by subtracting the average (mean) from within the window; for the depth channel we normalize by subtracting the median depth.”
Hobbit, a care robot supporting independent living at home: First prototype and lessons learned (2016, background): “[22] for mapping, since it is able to cope with uncertain odometry data and short-range depth data.”
Perception, Planning, Control, and Coordination for Autonomous Vehicles (2017, methodology): “A variation of the Particle Filter, Rao-Blackwellized Particle Filters(RBPF), has also been introduced as a solution to the SLAM problem in [150,151].”
Toward understanding natural language directions (2010, methodology): “The laser scans and odometry were used by a SLAM module in order to create a map as the robot explores the environment [14], while the camera was used to detect baseline objects in order to create a semantic map.”