RoboSLAM Research

SLAM Benchmarks: Closed Loop

Abstract: SLAM has long been envisioned as a means to assist robots performing navigation tasks in unknown environments. Yet, benchmarking for SLAM most often uses open-loop data sets due for reproducibility. Given that open-loop and closed-loop task performance measures may not align, establishing a means to use SLAM in the closed-loop for benchmarking is an important next step in algorithm development. A ROS/Gazebo environment for SLAM benchmarking has been created and open sourced. Several stereo visual-inertial implementations are tested and compared. The Good Feature SLAM approach provides competitive and robust closed-loop trajectory tracking in unknown environments and without external absolute position measurements.

Description coming soon. 1

  1. Y. Zhao, J.S. Smith, S.H. Karumanchi, and P.A. Vela. “Closed-Loop Benchmarking of Stereo Visual-Inertial SLAM Systems: Understanding the Impact of Drift and Latency on Tracking Accuracy” ICRA, 2020. arXiv. ↩︎