publications

2022

1. IROS

   Learned Depth Estimation of 3D Imaging Radar for Indoor Mapping

   Xu, R., Dong, W., Sharma, A., and Kaess, M.

   In Proc. IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, IROS Oct 2022

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   3D imaging radar offers robust perception capability through visually demanding environments due to the unique penetrative and reflective properties of millimeter waves (mmWave). Current approaches for 3D perception with imaging radar require knowledge of environment geometry, accumulation of data from multiple frames for perception, or access to between-frame motion. Imaging radar presents an additional difficulty due to the complexity of its data representation. To address these issues, and make imaging radar easier to use for downstream robotics tasks, we propose a learning-based method that regresses radar measurements into cylindrical depth maps using LiDAR supervision. Due to the limitation of the regression formulation, directions where the radar beam could not reach will still generate a valid depth. To address this issue, our method additionally learns a 3D filter to remove those pixels. Experiments show that our system generates visually accurate depth estimation. Furthermore, we confirm the overall ability to generalize in the indoor scene using the estimated depth for probabilistic occupancy mapping with ground truth trajectory.

2021

1. 

   Compositional and Scalable Object SLAM

   Sharma, A., Dong, W., and Kaess, M.

   In Proc. IEEE Intl. Conf. on Robotics and Automation, ICRA May 2021

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   We present a fast, scalable, and accurate Simultaneous Localization and Mapping (SLAM) system that represents indoor scenes as a graph of objects. Leveraging the observation that artificial environments are structured and occupied by recognizable objects, we show that a compositional and scalable object mapping formulation is amenable to a robust SLAM solution for drift-free large-scale indoor reconstruction. To achieve this , we propose a novel semantically assisted data association strategy that results in unambiguous persistent object landmarks and a 2.5D compositional rendering method that enables reliable frame-to-model RGB-D tracking. Consequently, we deliver an optimized online implementation that can run at near frame rate with a single graphics card, and provide a comprehensive evaluation against state-of-the-art baselines. An open-source implementation will be provided at https://github.com/rpl-cmu/object-slam.

2. M.S.

   Sharma, A. (2021). Incorporating Semantic Structure in SLAM (Number CMU-RI-TR-21-18). Carnegie Mellon University.

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