3 research outputs found
Characterizing SLAM Benchmarks and Methods for the Robust Perception Age
The diversity of SLAM benchmarks affords extensive testing of SLAM algorithms
to understand their performance, individually or in relative terms. The ad-hoc
creation of these benchmarks does not necessarily illuminate the particular
weak points of a SLAM algorithm when performance is evaluated. In this paper,
we propose to use a decision tree to identify challenging benchmark properties
for state-of-the-art SLAM algorithms and important components within the SLAM
pipeline regarding their ability to handle these challenges. Establishing what
factors of a particular sequence lead to track failure or degradation relative
to these characteristics is important if we are to arrive at a strong
understanding for the core computational needs of a robust SLAM algorithm.
Likewise, we argue that it is important to profile the computational
performance of the individual SLAM components for use when benchmarking. In
particular, we advocate the use of time-dilation during ROS bag playback, or
what we refer to as slo-mo playback. Using slo-mo to benchmark SLAM
instantiations can provide clues to how SLAM implementations should be improved
at the computational component level. Three prevalent VO/SLAM algorithms and
two low-latency algorithms of our own are tested on selected typical sequences,
which are generated from benchmark characterization, to further demonstrate the
benefits achieved from computationally efficient components.Comment: 7 pages, 5 figures, accepted at ICRA 2019 Workshop on Dataset
Generation and Benchmarking of SLAM Algorithms for Robotics and VR/A
Closed-Loop Benchmarking of Stereo Visual-Inertial SLAM Systems: Understanding the Impact of Drift and Latency on Tracking Accuracy
Visual-inertial SLAM is essential for robot navigation in GPS-denied
environments, e.g. indoor, underground. Conventionally, the performance of
visual-inertial SLAM is evaluated with open-loop analysis, with a focus on the
drift level of SLAM systems. In this paper, we raise the question on the
importance of visual estimation latency in closed-loop navigation tasks, such
as accurate trajectory tracking. To understand the impact of both drift and
latency on visual-inertial SLAM systems, a closed-loop benchmarking simulation
is conducted, where a robot is commanded to follow a desired trajectory using
the feedback from visual-inertial estimation. By extensively evaluating the
trajectory tracking performance of representative state-of-the-art
visual-inertial SLAM systems, we reveal the importance of latency reduction in
visual estimation module of these systems. The findings suggest directions of
future improvements for visual-inertial SLAM.Comment: 8 pages, 7 figures. Accepted for publication in ICRA 202
Good Graph to Optimize: Cost-Effective, Budget-Aware Bundle Adjustment in Visual SLAM
The cost-efficiency of visual(-inertial) SLAM (VSLAM) is a critical
characteristic of resource-limited applications. While hardware and algorithm
advances have been significantly improved the cost-efficiency of VSLAM
front-ends, the cost-efficiency of VSLAM back-ends remains a bottleneck. This
paper describes a novel, rigorous method to improve the cost-efficiency of
local BA in a BA-based VSLAM back-end. An efficient algorithm, called Good
Graph, is developed to select size-reduced graphs optimized in local BA with
condition preservation. To better suit BA-based VSLAM back-ends, the Good Graph
predicts future estimation needs, dynamically assigns an appropriate size
budget, and selects a condition-maximized subgraph for BA estimation.
Evaluations are conducted on two scenarios: 1) VSLAM as standalone process, and
2) VSLAM as part of closed-loop navigation system. Results from the first
scenario show Good Graph improves accuracy and robustness of VSLAM estimation,
when computational limits exist. Results from the second scenario, indicate
that Good Graph benefits the trajectory tracking performance of VSLAM-based
closed-loop navigation systems, which is a primary application of VSLAM.Comment: 20 pages, 14 figures, 8 tables. Submitted to IEEE Transactions on
Robotics, for the provided open-source software see
https://github.com/ivalab/gf_orb_slam