Category Archives: Mobile Robot Slam


H. A. G. C. Premachandra, R. Liu, C. Yuen and U. -X. Tan, UWB Radar SLAM: An Anchorless Approach in Vision Denied Indoor Environments, IEEE Robotics and Automation Letters, vol. 8, no. 9, pp. 5299-5306, Sept. 2023 DOI: 10.1109/LRA.2023.3293354.

LiDAR and cameras are frequently used as sensors for simultaneous localization and mapping (SLAM). However, these sensors are prone to failure under low visibility (e.g. smoke) or places with reflective surfaces (e.g. mirrors). On the other hand, electromagnetic waves exhibit better penetration properties when the wavelength increases, thus are not affected by low visibility. Hence, this letter presents ultra-wideband (UWB) radar as an alternative to the existing sensors. UWB is generally known to be used in anchor-tag SLAM systems. One or more anchors are installed in the environment and the tags are attached to the robots. Although this method performs well under low visibility, modifying the existing infrastructure is not always feasible. UWB has also been used in peer-to-peer ranging collaborative SLAM systems. However, this requires more than a single robot and does not include mapping in the mentioned environment like smoke. Therefore, the presented approach in this letter solely depends on the UWB transceivers mounted on-board. In addition, an extended Kalman filter (EKF) SLAM is used to solve the SLAM problem at the back-end. Experiments were conducted and demonstrated that the proposed UWB-based radar SLAM is able to map natural point landmarks inside an indoor environment while improving robot localization.

Comprehensive survey of the history and state of the art of active SLAM

J. A. Placed et al., A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers, IEEE Transactions on Robotics, vol. 39, no. 3, pp. 1686-1705 DOI: 10.1109/TRO.2023.3248510.

Active simultaneous localization and mapping (SLAM) is the problem of planning and controlling the motion of a robot to build the most accurate and complete model of the surrounding environment. Since the first foundational work in active perception appeared, more than three decades ago, this field has received increasing attention across different scientific communities. This has brought about many different approaches and formulations, and makes a review of the current trends necessary and extremely valuable for both new and experienced researchers. In this article, we survey the state of the art in active SLAM and take an in-depth look at the open challenges that still require attention to meet the needs of modern applications. After providing a historical perspective, we present a unified problem formulation and review the well-established modular solution scheme, which decouples the problem into three stages that identify, select, and execute potential navigation actions. We then analyze alternative approaches, including belief-space planning and deep reinforcement learning techniques, and review related work on multirobot coordination. This article concludes with a discussion of new research directions, addressing reproducible research, active spatial perception, and practical applications, among other topics.

A survey on visual SLAM in robotics

Iman Abaspur Kazerouni, Luke Fitzgerald, Gerard Dooly, Daniel Toal, A survey of state-of-the-art on visual SLAM, Expert Systems with Applications, Volume 205, 2022 DOI: 10.1016/j.eswa.2022.117734.

This paper is an overview to Visual Simultaneous Localization and Mapping (V-SLAM). We discuss the basic definitions in the SLAM and vision system fields and provide a review of the state-of-the-art methods utilized for mobile robot\u2019s vision and SLAM. This paper covers topics from the basic SLAM methods, vision sensors, machine vision algorithms for feature extraction and matching, Deep Learning (DL) methods and datasets for Visual Odometry (VO) and Loop Closure (LC) in V-SLAM applications. Several feature extraction and matching algorithms are simulated to show a better vision of feature-based techniques.

See also:

Jun Cheng, Liyan Zhang, Qihong Chen, Xinrong Hu, Jingcao Cai, “A review of visual SLAM methods for autonomous driving vehicles,” Engineering Applications of Artificial Intelligence, Volume 114, 2022, 104992, ISSN 0952-1976,

Tianyao Zhang, Xiaoguang Hu, Jin Xiao, Guofeng Zhang, “A survey of visual navigation: From geometry to embodied AI,” Engineering Applications of Artificial Intelligence, Volume 114, 2022, 105036, ISSN 0952-1976,

Continuous POMDPs through belief state sparsification, applied to active SLAM

Elimelech K, Indelman V. Simplified decision making in the belief space using belief sparsification. The International Journal of Robotics Research. 2022;41(5):470-496 DOI: 10.1177/02783649221076381.

In this work, we introduce a new and efficient solution approach for the problem of decision making under uncertainty, which can be formulated as decision making in a belief space, over a possibly high-dimensional state space. Typically, to solve a decision problem, one should identify the optimal action from a set of candidates, according to some objective. We claim that one can often generate and solve an analogous yet simplified decision problem, which can be solved more efficiently. A wise simplification method can lead to the same action selection, or one for which the maximal loss in optimality can be guaranteed. Furthermore, such simplification is separated from the state inference and does not compromise its accuracy, as the selected action would finally be applied on the original state. First, we present the concept for general decision problems and provide a theoretical framework for a coherent formulation of the approach. We then practically apply these ideas to decision problems in the belief space, which can be simplified by considering a sparse approximation of their initial belief. The scalable belief sparsification algorithm we provide is able to yield solutions which are guaranteed to be consistent with the original problem. We demonstrate the benefits of the approach in the solution of a realistic active-SLAM problem and manage to significantly reduce computation time, with no loss in the quality of solution. This work is both fundamental and practical and holds numerous possible extensions.

A nice summary of SLAM in robotics with Lidar and Cameras

Chghaf, M., Rodriguez, S. & Ouardi, A.E. Camera, LiDAR and Multi-modal SLAM Systems for Autonomous Ground Vehicles: a Survey J Intell Robot Syst 105, 2 (2022) DOI: 10.1007/s10846-022-01582-8.

Simultaneous Localization and Mapping (SLAM) have been widely studied over the last years for autonomous vehicles. SLAM achieves its purpose by constructing a map of the unknown environment while keeping track of the location. A major challenge, which is paramount during the design of SLAM systems, lies in the efficient use of onboard sensors to perceive the environment. The most widely applied algorithms are camera-based SLAM and LiDAR-based SLAM. Recent research focuses on the fusion of camera-based and LiDAR-based frameworks that show promising results. In this paper, we present a study of commonly used sensors and the fundamental theories behind SLAM algorithms. The study then presents the hardware architectures used to process these algorithms and the performance obtained when possible. Secondly, we highlight state-of-the-art methodologies in each modality and in the multi-modal framework. A brief comparison followed by future challenges is then underlined. Additionally, we provide insights to possible fusion approaches that can increase the robustness and accuracy of modern SLAM algorithms; hence allowing the hardware-software co-design of embedded systems taking into account the algorithmic complexity and the embedded architectures and real-time constraints.

More efficient pose-graph optimization by using the cycles (loop closures) in the graph as a basis, and a nice summary of conventional pose-graph optimization

F. Bai, T. Vidal-Calleja and G. Grisetti, Sparse Pose Graph Optimization in Cycle Space, .IEEE Transactions on Robotics, vol. 37, no. 5, pp. 1381-1400, Oct 2021 DOI: 10.1109/TRO.2021.3050328.

The state-of-the-art modern pose-graph optimization (PGO) systems are vertex based. In this context, the number of variables might be high, albeit the number of cycles in the graph (loop closures) is relatively low. For sparse problems particularly, the cycle space has a significantly smaller dimension than the number of vertices. By exploiting this observation, in this article, we propose an alternative solution to PGO that directly exploits the cycle space. We characterize the topology of the graph as a cycle matrix, and reparameterize the problem using relative poses, which are further constrained by a cycle basis of the graph. We show that by using a minimum cycle basis, the cycle-based approach has superior convergence properties against its vertex-based counterpart, in terms of convergence speed and convergence to the global minimum. For sparse graphs, our cycle-based approach is also more time efficient than the vertex-based. As an additional contribution of this work, we present an effective algorithm to compute the minimum cycle basis. Albeit known in computer science, we believe that this algorithm is not familiar to the robotics community. All the claims are validated by experiments on both standard benchmarks and simulated datasets. To foster the reproduction of the results, we provide a complete open-source C++ implementation 1 of our approach.

Dropping laser scans for SLAM when they contribute with no relevant information

Kirill Krinkin, Anton Filatov, Correlation filter of 2D laser scans for indoor environment, . Robotics and Autonomous Systems, Volume 142, 2021 DOI: 10.1016/j.robot.2021.103809.

Modern laser SLAM (simultaneous localization and mapping) and structure from motion algorithms face the problem of processing redundant data. Even if a sensor does not move, it still continues to capture scans that should be processed. This paper presents the novel filter that allows dropping 2D scans that bring no new information to the system. Experiments on MIT and TUM datasets show that it is possible to drop more than half of the scans. Moreover the paper describes the formulas that enable filter adaptation to a particular robot with known speed and characteristics of lidar. In addition, the indoor corridor detector is introduced that also can be applied to any specific shape of a corridor and sensor.

Simultaneous localization, mapping and semantic labelling in mobile robots

Taniguchi, Akira, Hagiwara, Yoshinobu, Taniguchi, Tadahiro, Inamura, Tetsunari, Improved and scalable online learning of spatial concepts and language models with mapping, Autonomous Robots 44(6), DOI: 10.1007/s10514-020-09905-0.

We propose a novel online learning algorithm, called SpCoSLAM 2.0, for spatial concepts and lexical acquisition with high accuracy and scalability. Previously, we proposed SpCoSLAM as an online learning algorithm based on unsupervised Bayesian probabilistic model that integrates multimodal place categorization, lexical acquisition, and SLAM. However, our original algorithm had limited estimation accuracy owing to the influence of the early stages of learning, and increased computational complexity with added training data. Therefore, we introduce techniques such as fixed-lag rejuvenation to reduce the calculation time while maintaining an accuracy higher than that of the original algorithm. The results show that, in terms of estimation accuracy, the proposed algorithm exceeds the original algorithm and is comparable to batch learning. In addition, the calculation time of the proposed algorithm does not depend on the amount of training data and becomes constant for each step of the scalable algorithm. Our approach will contribute to the realization of long-term spatial language interactions between humans and robots.

A possibly interesting paper on the estimation and adaptation of EKF-SLAM to actual models of the system and the noise that I have been unable to read due to its painful syntax

Yingzhong Tian, Heru Suwoyo, Wenbin Wang, Dziki Mbemba, Long Li, An AEKF-SLAM Algorithm with Recursive Noise Statistic Based on MLE and EM, Journal of Intelligent & Robotic Systems (2020) 97:339–355, DOI: 10.1007/s10846-019-01044-8.

Extended Kalman Filter (EKF) has been popularly utilized for solving Simultaneous Localization and Mapping (SLAM)
problem. Essentially, it requires the accurate system model and known noise statistic. Nevertheless, this condition can
be satisfied in simulation case. Hence, EKF has to be enhanced when it is applied in the real-application. Mainly, this
improvement is known as adaptive-based approach. In many different cases, it is indicated by some manners of estimating
for either part or full noise statistic. This paper present a proposed method based on the adaptive-based solution used for
improving classical EKF namely An Adaptive Extended Kalman Filter. Initially, the classical EKF was improved based on
Maximum Likelihood Estimation (MLE) and Expectation-Maximization (EM) Creation. It aims to equips the conventional
EKF with ability of approximating noise statistic and its covariance matrices recursively. Moreover, EKF was modified and
improved to tune the estimated values given by MLE and EM creation. Besides that, the recursive noise statistic estimators
were also estimated based on the unbiased estimation. Although it results high quality solution but it is followed with some
risks of non-positive definite matrices of the process and measurement noise statistic covariances. Thus, an addition of
Innovation Covariance Estimation (ICE) was also utilized to depress this possibilities. The proposed method is applied for
solving SLAM problem of autonomous wheeled mobile robot. Henceforth, it is termed as AEKF-SLAM Algorithm. In order
to validate the effectiveness of proposed method, some different SLAM-Based algorithm were compared and analyzed.
The different simulation has been showing that the proposed method has better stability and accuracy compared to the
conventional filter in term of Root Mean Square Error (RMSE) of Estimated Map Coordinate (EMC) and Estimated Path
Coordinate (EPC).

Interesting summary of SLAM and its computational cost approaches

Joan Vallvé, Joan Solà, Juan Andrade-Cetto, Pose-graph SLAM sparsification using factor descent. Robotics and Autonomous Systems, Volume 119, 2019, Pages 108-118, DOI: 10.1016/j.robot.2019.06.004.

Since state of the art simultaneous localization and mapping (SLAM) algorithms are not constant time, it is often necessary to reduce the problem size while keeping as much of the original graph\u2019s information content. In graph SLAM, the problem is reduced by removing nodes and rearranging factors. This is normally faced locally: after selecting a node to be removed, its Markov blanket sub-graph is isolated, the node is marginalized and its dense result is sparsified. The aim of sparsification is to compute an approximation of the dense and non-relinearizable result of node marginalization with a new set of factors. Sparsification consists on two processes: building the topology of new factors, and finding the optimal parameters that best approximate the original dense distribution. This best approximation can be obtained through minimization of the Kullback\u2013Liebler divergence between the two distributions. Using simple topologies such as Chow\u2013Liu trees, there is a closed form for the optimal solution. However, a tree is oftentimes too sparse and produces bad distribution approximations. On the contrary, more populated topologies require nonlinear iterative optimization. In the present paper, the particularities of pose-graph SLAM are exploited for designing new informative topologies and for applying the novel factor descent iterative optimization method for sparsification. Several experiments are provided comparing the proposed topology methods and factor descent optimization with state-of-the-art methods in synthetic and real datasets with regards to approximation accuracy and computational cost.