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1. Pairwise Consistent Measurement Set Maximization for Robust Multi-Robot Map Merging

   https://doi.org/10.1109/ICRA.2018.8460217  

   Mangelson, Joshua G.; Dominic, Derrick; Eustice, Ryan M.; Vasudevan, Ram (May 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA))

   This paper reports on a method for robust selection of inter-map loop closures in multi-robot simultaneous localization and mapping (SLAM). Existing robust SLAM methods assume a good initialization or an “odometry backbone” to classify inlier and outlier loop closures. In the multi-robot case, these assumptions do not always hold. This paper presents an algorithm called Pairwise Consistency Maximization (PCM) that estimates the largest pairwise internally consistent set of measurements. Finding the largest pairwise internally consistent set can be transformed into an instance of the maximum clique problem from graph theory, and by leveraging the associated literature it can be solved in real time. This paper evaluates how well PCM approximates the combinatorial gold standard using simulated data. It also evaluates the performance of PCM on synthetic and real-world data sets in comparison with DCS, SCGP, and RANSAC, and shows that PCM significantly outperforms these methods. 

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2. Online and Distributed Robust Regressions with Extremely Noisy Labels

   https://doi.org/10.1145/3473038  

   Lei, Shuo; Zhang, Xuchao; Zhao, Liang; Boedihardjo, Arnold P.; Lu, Chang-Tien (June 2022, ACM Transactions on Knowledge Discovery from Data)

   In today’s era of big data, robust least-squares regression becomes a more challenging problem when considering the extremely corrupted labels along with explosive growth of datasets. Traditional robust methods can handle the noise but suffer from several challenges when applied in huge dataset including (1) computational infeasibility of handling an entire dataset at once, (2) existence of heterogeneously distributed corruption, and (3) difficulty in corruption estimation when data cannot be entirely loaded. This article proposes online and distributed robust regression approaches, both of which can concurrently address all the above challenges. Specifically, the distributed algorithm optimizes the regression coefficients of each data block via heuristic hard thresholding and combines all the estimates in a distributed robust consolidation. In addition, an online version of the distributed algorithm is proposed to incrementally update the existing estimates with new incoming data. Furthermore, a novel online robust regression method is proposed to estimate under a biased-batch corruption. We also prove that our algorithms benefit from strong robustness guarantees in terms of regression coefficient recovery with a constant upper bound on the error of state-of-the-art batch methods. Extensive experiments on synthetic and real datasets demonstrate that our approaches are superior to those of existing methods in effectiveness, with competitive efficiency. 

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3. A Fast and Robust Place Recognition Approach for Stereo Visual Odometry Using LiDAR Descriptors

   https://doi.org/10.1109/IROS45743.2020.9341733  

   Mo, Jiawei; Sattar, Junaed (October 2020, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   null (Ed.)

   Place recognition is a core component of Simultaneous Localization and Mapping (SLAM) algorithms. Particularly in visual SLAM systems, previously-visited places are recognized by measuring the appearance similarity between images representing these locations. However, such approaches are sensitive to visual appearance change and also can be computationally expensive. In this paper, we propose an alternative approach adapting LiDAR descriptors for 3D points obtained from stereo-visual odometry for place recognition. 3D points are potentially more reliable than 2D visual cues (e.g., 2D features) against environmental changes (e.g., variable illumination) and this may benefit visual SLAM systems in long-term deployment scenarios. Stereo-visual odometry generates 3D points with an absolute scale, which enables us to use LiDAR descriptors for place recognition with high computational efficiency. Through extensive evaluations on standard benchmark datasets, we demonstrate the accuracy, efficiency, and robustness of using 3D points for place recognition over 2D methods. 

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4. Fault-Tolerant Mapping and Localization for Quadrotor UAVs

   Gilson, Max; Gauthier, Jason; Garcia, Kaylee; Kienzle, Cory; Muse, Jonathan A; Zhang, Xiaodong (January 2021, AIAA Scitech 2021 Forum)

   null (Ed.)

   This paper presents a fault-tolerant control method for a quadrotor UAV using solely on-board sensors. A simultaneous localization and mapping (SLAM) system is developed utilizing a laser rangefinder and an open source SLAM algorithm called GMapping. This system allows for mapping of the surrounding environment as well as localizing the position of the quadrotor, enabling real-time position control. However, the SLAM system using the laser rangefinder may fail in certain degenerate environment like featureless tunnels or straight hallways. In order to compensate for possible faults in the SLAM measurements, a fault detection and fault-tolerant control method is developed. An observer is designed to estimate the translational velocity of the quadrotor using SLAM position measurements. The fault detection residual is defined as the deviation between this SLAM-based velocity estimate and another velocity estimate generated by an optical flow algorithm utilizing measurements provided by a downward facing camera. Real-time experimental results have shown the effectiveness of the fault-tolerant control algorithm. 

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5. IV-SLAM: Introspective Vision for Simultaneous Localization and Mapping

   Rabiee, Sadegh; Biswas, Joydeep (November 2020, Conference on Robot Learning)

   Existing solutions to visual simultaneous localization and mapping (VSLAM) assume that errors in feature extraction and matching are independent and identically distributed (i.i.d), but this assumption is known to not be true – features extracted from low-contrast regions of images exhibit wider error distributions than features from sharp corners. Furthermore, V-SLAM algorithms are prone to catastrophic tracking failures when sensed images include challenging conditions such as specular reflections, lens flare, or shadows of dynamic objects. To address such failures, previous work has focused on building more robust visual frontends, to filter out challenging features. In this paper, we present introspective vision for SLAM (IV-SLAM), a fundamentally different approach for addressing these challenges. IV-SLAM explicitly models the noise process of reprojection errors from visual features to be context-dependent, and hence non-i.i.d. We introduce an autonomously supervised approach for IV-SLAM to collect training data to learn such a context-aware noise model. Using this learned noise model, IV-SLAM guides feature extraction to select more features from parts of the image that are likely to result in lower noise, and further incorporate the learned noise model into the joint maximum likelihood estimation, thus making it robust to the aforementioned types of errors. We present empirical results to demonstrate that IV-SLAM 1) is able to accurately predict sources of error in input images, 2) reduces tracking error compared to V-SLAM, and 3) increases the mean distance between tracking failures by more than 70% on challenging real robot data compared to V-SLAM. 

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