5 citations · 9 across the 6 of their papers we have counts for
6 papers
GAPSLAM: Blending Gaussian Approximation and Particle Filters for Real-Time Non-Gaussian SLAM
Qiangqiang Huang, John J. Leonard
Inferring the posterior distribution in SLAM is critical for evaluating the uncertainty in localization and mapping, as well as supporting subsequent planning tasks aiming to reduc…
Optimizing Fiducial Marker Placement for Improved Visual Localization
Qiangqiang Huang, Joseph DeGol, Victor Fragoso +2
Adding fiducial markers to a scene is a well-known strategy for making visual localization algorithms more robust. Traditionally, these marker locations are selected by humans who…
Incremental Non-Gaussian Inference for SLAM Using Normalizing Flows
Qiangqiang Huang, Can Pu, Kasra Khosoussi +4
This paper presents normalizing flows for incremental smoothing and mapping (NF-iSAM), a novel algorithm for inferring the full posterior distribution in SLAM problems with nonline…
Nested Sampling for Non-Gaussian Inference in SLAM Factor Graphs
Qiangqiang Huang, Alan Papalia, John J. Leonard
We present nested sampling for factor graphs (NSFG), a novel nested sampling approach to approximate inference for posterior distributions expressed over factor-graphs. Performing…
A Multi-Hypothesis Approach to Pose Ambiguity in Object-Based SLAM
Jiahui Fu, Qiangqiang Huang, Kevin Doherty +2
In object-based Simultaneous Localization and Mapping (SLAM), 6D object poses offer a compact representation of landmark geometry useful for downstream planning and manipulation ta…
Consensus-Informed Optimization Over Mixtures for Ambiguity-Aware Object SLAM
Ziqi Lu, Qiangqiang Huang, Kevin Doherty +1
Building object-level maps can facilitate robot-environment interactions (e.g. planning and manipulation), but objects could often have multiple probable poses when viewed from a s…