9 accepted papers
Scaling to arbitrarily large bundle adjustment problems requires data and compute to be distributed across multiple devices. Centralized methods in prior works are only able to solve small or medium size problems due to overhead in computation and communication. In this paper, we present a fully dec…
Simultaneous localization and mapping (SLAM) is a crucial functionality for exploration robots and virtual/augmented reality (VR/AR) devices. However, some of such devices with limited resources cannot afford the computational or memory cost to run full SLAM algorithms. We propose a general client-s…
We present multi-robot iSAM2 (MR-iSAM2), an efficient incremental smoothing and mapping (iSAM) algorithm to solve multi-robot simultaneous localization and mapping (SLAM) inference problems. MR-iSAM2 is based on a novel data structure multi-root Bayes tree (MRBT), which packs multiple Bayes trees wi…
In this paper, we introduce an ambiguity-aware robust active SLAM (ARAS) framework that makes use of multi-hypothesis state and map estimations to achieve better robustness. Ambiguous measurements can result in multiple probable solutions in a multi-hypothesis SLAM (MH-SLAM) system if they are tempo…
A novel nonlinear incremental optimization algorithm MH-iSAM2 is developed to handle ambiguity in simultaneous localization and mapping (SLAM) problems in a multi-hypothesis fashion. It can output multiple possible solutions for each variable according to the ambiguous inputs, which is expected to g…
In this work, we develop a novel dense planar-inertial SLAM (DPI-SLAM) system to reconstruct dense 3D models of large indoor environments using a hand-held RGB-D sensor and an inertial measurement unit (IMU). The preinte-grated IMU measurements are loosely-coupled with the dense visual odometry (VO)…
In this paper, we present a novel approach to tightly couple visual and inertial measurements in a fixed-lag visual-inertial odometry (VIO) framework using information sparsification. To bound computational complexity, fixed-lag smoothers typically marginalize out variables, but consequently introdu…
In this paper, we propose a mapping approach that constructs a globally deformable virtual occupancy grid map (VOG-map) based on local submaps. Such a representation allows pose graph SLAM systems to correct globally accumulated drift via loop closures while maintaining free space information for th…
In this work, we develop a novel keyframe-based dense planar SLAM (KDP-SLAM) system, based on CPU only, to reconstruct large indoor environments in real-time using a hand-held RGB-D sensor. Our keyframe-based approach applies a fast dense method to estimate odometry, fuses depth measurements from sm…