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   Adkins, Amanda ; Chen, Taijing ; Biswas, Joydeep ( March 2024 , IEEE Robotics and Automation Letters)
2. Fast Direct Stereo Visual SLAM

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   Mo, Jiawei ; Islam, Md Jahidul ; Sattar, Junaed ( April 2022 , IEEE Robotics and Automation Letters)
3. DRACo-SLAM: Distributed Robust Acoustic Communication-efficient SLAM for Imaging Sonar Equipped Underwater Robot Teams

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4. Periodic SLAM: Using Cyclic Constraints to Improve the Performance of Visual-Inertial SLAM on Legged Robots

   Kumar, Hans ; Payne, J. Joe ; Travers, Matthew ; Johnson, Aaron M. ; Choset, Howie ( May 2022 , IEEE International Conference on Robotics and Automation)

   Methods for state estimation that rely on visual information are challenging on legged robots due to rapid changes in the viewing angle of onboard cameras. In this work, we show that by leveraging structure in the way that the robot locomotes, the accuracy of visual-inertial SLAM in these challenging scenarios can be increased. We present a method that takes advantage of the underlying periodic predictability often present in the motion of legged robots to improve the performance of the feature tracking module within a visual-inertial SLAM system. Our method performs multi-session SLAM on a single robot, where each session is responsible for mapping during a distinct portion of the robot’s gait cycle. Our method produces lower absolute trajectory error than several state-of-the-art methods for visual-inertial SLAM in both a simulated environment and on data collected on a quadrupedal robot executing dynamic gaits. On real-world bounding gaits, our median trajectory error was less than 35% of the error of the next best estimate provided by state-of-the-art methods. 

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5. Incremental scene understanding on dense SLAM

   https://doi.org/10.1109/IROS.2016.7759111  

   Li, Chi ; Xiao, Han ; Tateno, Keisuke ; Tombari, Federico ; Navab, Nassir ; Hager, Gregory D. ( October 2016 , IROS)

   We present an architecture for online, incremental scene modeling which combines a SLAM-based scene understanding framework with semantic segmentation and object pose estimation. The core of this approach comprises a probabilistic inference scheme that predicts semantic labels for object hypotheses at each new frame. From these hypotheses, recognized scene structures are incrementally constructed and tracked. Semantic labels are inferred using a multi-domain convolutional architecture which operates on the image time series and which enables efficient propagation of features as well as robust model registration. To evaluate this architecture, we introduce a large-scale RGB-D dataset JHUSEQ-25 as a new benchmark for the sequence-based scene understanding in complex and densely cluttered scenes. This dataset contains 25 RGB-D video sequences with 100,000 labeled frames in total. We validate our method on this dataset and demonstrate improved performance of semantic segmentation and 6-DoF object pose estimation compared with methods based on the single view. 

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