Double window optimisation for constant time visual SLAM

By Hauke Strasdat, Andrew J. Davison, José M. M. Montiel, Kurt Konolige. Vision, 2011

332 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/62b3f8d2206d8913c562c43d285be329904ab8db

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras (2017, background): “proposed to expand the concept of keyframes to integrate scale [26] and proposed a double window optimization [25].”
ORB-SLAM: A Versatile and Accurate Monocular SLAM System (2015, methodology): “[6], [7], the optimization framework g2o by Kuemmerle et al.”
PL-SLAM: A Stereo SLAM System Through the Combination of Points and Line Segments (2017, background): “Finally, the covisibility information is modeled by a graph (as in [38]), where each node represents a KF, and the edges between KFs are created only if they share a minimum number”
ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras (2016, methodology): “[8] performs a joint optimization of BA (point-pose constraints) in an inner window of keyframes and pose graph (pose-pose constraints) in an outer window.”
Vox-Fusion: Dense Tracking and Mapping with Voxel-based Neural Implicit Representation (2022, methodology): “These keyframes, including the recently tracked frame, can be seen as an optimization window akin to the sliding window approach employed in traditional SLAM systems [27].”
Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017, methodology): “They combine methods from loop detection (Gálvez-López & Tardós (2012)), loop closing (Strasdat et al. (2010, 2011)) and pose graph optimization (Kümmerle et al. (2011)) into one system.”
LDSO: Direct Sparse Odometry with Loop Closure (2018, methodology): “Many mature feature-based monocular SLAM systems have been presented in recent years, often inspired by the seminal PTAM [4], where splitting the system into a camera 1https://vision.in.tum.de/research/vslam/ldso tracking frontend and an optimization-based mapping backend was originally proposed, an”
Direct Sparse Odometry (2016, methodology): “be integrated with other optimization frameworks like (double-windowed) bundle adjustment [25] or incremental smoothing and mapping [14].”
ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual–Inertial, and Multimap SLAM (2020, methodology): “Large scale monocular SLAM was achieved in [57] using sliding-window BA, and in [58] using a double-window optimization and a covisibility graph.”
ElasticFusion: Dense SLAM Without A Pose Graph (2015, unknown): “’s work on double window optimisation [20].”
Multiple‐Robot Simultaneous Localization and Mapping: A Review (2016, background): “, 2011), ScaViSLAM (Strasdat et al., 2011), fast odometry (Dryanovski et al.”
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016, unknown): “[235] take a two-stage approach and optimize first a local pose-features graph and then a pose-pose graph; Williams et al.”
Large-scale direct SLAM for omnidirectional cameras (2015, unknown): “This can be done in a filtering framework [3], [4], [5], or in a keyframe-based non-linear optimization framework [6], [7], [8].”
ElasticFusion: Real-time dense SLAM and light source estimation (2016, methodology): “To evaluate the trajectory estimation performance of our approach we test our system on the RGB-D benchmark of Sturm et al. (2012). This benchmark provides synchronized ground truth poses for an RGB-D sensor moved through a scene, captured with a highly precise motion capture system.”