Vision-based global localization and mapping for mobile robots

By Stephen Se, David G. Lowe, James J. Little. IEEE Transactions on robotics, 2005

567 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/2b79491ea6c5727d7c0b0df2037a73258644415f

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance (2008, methodology): “This problem has recently received attention in the context of large-scale global localization (Schindler et al. 2007) and loop closure detection in mobile robotics (Ho and Newman 2007).”
Visual simultaneous localization and mapping: a survey (2012, methodology): “Among the exteroceptive sensors it is possible to ﬁnd: sonar (Tardós et al. 2002; Ribas et al. 2008), range lasers (Nüchter et al. 2007; Thrun et al. 2006), cameras (Se et al. 2005; Lemaire et al. 2007; Davison 2003; Bogdan et al. 2009) and global positioning systems (GPS) (Thrun et al. 2005a).”
Learning Semantic Maps from Natural Language Descriptions (2013, background): “A number of solutions exist that identify loop closures based upon visual appearance [19, 20] and local metric structure [21], among others.”
Get Out of My Lab: Large-scale, Real-Time Visual-Inertial Localization (2015, methodology): “The latter methods run SLAM on the device, enabling them to handle the latency of transmitting images to the server and to track the pose for periods where localization against the global model fails [32, 44, 52].”
A Theoretical Foundation of Network Localization and Navigation (2018, unknown): “Inertial measurements can be used to reconstruct the agent’s trajectory, while the feature points in multiple images can be used to estimate the agent movement [81]–[83].”
Robust reconstruction of indoor scenes (2015, background): “Fragments are analogous to submaps, which are used in a number of robotic mapping systems [25, 5, 54, 10].”
Incremental smoothing and mapping (2008, methodology): “[120, 121] that uses SIFT features in combination with a trinocular sensor.”
Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images (2013, background): “Efficient keypoint-based relocalization modules have been proposed for visual SLAM [9, 33] and robotics [30].”
Visual Navigation for Mobile Robots: A Survey (2008, background): “During the robot navigation process, detected invariant features are observed from different points of view, angles, distances and under different illumination conditions and thus become highly appropriate landmarks to be tracked for navigation, global localization [117] and robust vision-based SLAM”
Learning to Navigate the Energy Landscape (2016, methodology): “We refer the interested reader to [15, 14, 25, 10, 48, 37, 47] and [42, 39] for relevant litterature on RGB camera relocalization and Hand Pose Estimation, respectively.”
The SLAM problem: a survey (2008, background): “Some recent publications tackle the problem by using multiple maps, or sub-maps that are lately used to build a larger global map [4] [32] [33].”
Sub-meter indoor localization in unmodified environments with inexpensive sensors (2010, methodology): “Others have localized by direct image matching, without using a probabilistic filter or motion model [15], [16].”