Category Archives: Mobile Robot Slam

Interesting study about how to quantify the uncertainty in SLAM and the preservation of its monotonic growth, which is needed to good decision making in active SLAM

M. L. Rodríguez-Arévalo, J. Neira and J. A. Castellanos, On the Importance of Uncertainty Representation in Active SLAM, IEEE Transactions on Robotics, vol. 34, no. 3, pp. 829-834 DOI: 10.1109/TRO.2018.2808902.

The purpose of this work is to highlight the paramount importance of representing and quantifying uncertainty to correctly report the associated confidence of the robot’s location estimate at each time step along its trajectory and therefore decide the correct course of action in an active SLAM mission. We analyze the monotonicity property of different decision-making criteria, both in 2-D and 3-D, with respect to the representation of uncertainty and of the orientation of the robot’s pose. Monotonicity, the property that uncertainty increases as the robot moves, is essential for adequate decision making. We analytically show that, by using differential representations to propagate spatial uncertainties, monotonicity is preserved for all optimality criteria, A-opt, D-opt, and E-opt, and for Shannon’s entropy. We also show that monotonicity does not hold for any criteria in absolute representations using Roll-Pitch-Yaw and Euler angles. Finally, using unit quaternions in absolute representations, the only criteria that preserve monotonicity are D-opt and Shannon’s entropy.

Using short- and long-term memories in SLAM

Labbé, M. & Michaud, F., Long-term online multi-session graph-based SPLAM with memory management, Auton Robot (2018) 42: 1133. DOI: 10.1007/s10514-017-9682-5.

For long-term simultaneous planning, localization and mapping (SPLAM), a robot should be able to continuously update its map according to the dynamic changes of the environment and the new areas explored. With limited onboard computation capabilities, a robot should also be able to limit the size of the map used for online localization and mapping. This paper addresses these challenges using a memory management mechanism, which identifies locations that should remain in a Working Memory (WM) for online processing from locations that should be transferred to a Long-Term Memory (LTM). When revisiting previously mapped areas that are in LTM, the mechanism can retrieve these locations and place them back in WM for online SPLAM. The approach is tested on a robot equipped with a short-range laser rangefinder and a RGB-D camera, patrolling autonomously 10.5 km in an indoor environment over 11 sessions while having encountered 139 people.

Faster long-term SLAM through direct use of Lie groups in filtering

Kruno Lenac, Josip Ćesić, Ivan Marković, and Ivan Petrović, Exactly sparse delayed state filter on Lie groups for long-term pose graph SLAM, The International Journal of Robotics Research Vol 37, Issue 6, pp. 585 – 610 DOI: 10.1177/0278364918767756.

In this paper we propose a simultaneous localization and mapping (SLAM) back-end solution called the exactly sparse delayed state filter on Lie groups (LG-ESDSF). We derive LG-ESDSF and demonstrate that it retains all the good characteristics of the classic Euclidean ESDSF, the main advantage being the exact sparsity of the information matrix. The key advantage of LG-ESDSF in comparison with the classic ESDSF lies in the ability to respect the state space geometry by negotiating uncertainties and employing filtering equations directly on Lie groups. We also exploit the special structure of the information matrix in order to allow long-term operation while the robot is moving repeatedly through the same environment. To prove the effectiveness of the proposed SLAM solution, we conducted extensive experiments on two different publicly available datasets, namely the KITTI and EuRoC datasets, using two front-ends: one based on the stereo camera and the other on the 3D LIDAR. We compare LG-ESDSF with the general graph optimization framework (g2o) when coupled with the same front-ends. Similarly to g2o the proposed LG-ESDSF is front-end agnostic and the comparison demonstrates that our solution can match the accuracy of g2o, while maintaining faster computation times. Furthermore, the proposed back-end coupled with the stereo camera front-end forms a complete visual SLAM solution dubbed LG-SLAM. Finally, we evaluated LG-SLAM using the online KITTI protocol and at the time of writing it achieved the second best result among the stereo odometry solutions and the best result among the tested SLAM algorithms.

Rao-Blackwellized Particle Filter SLAM with grid maps in which particles do not contain the whole map but only a part

H. Jo, H. M. Cho, S. Jo and E. Kim, Efficient Grid-Based Rao–Blackwellized Particle Filter SLAM With Interparticle Map Sharing, IEEE/ASME Transactions on Mechatronics, vol. 23, no. 2, pp. 714-724, DOI: 10.1109/TMECH.2018.2795252.

In this paper, we propose a novel and efficient grid-based Rao-Blackwellized particle filter simultaneous localization and mapping (RBPF-SLAM) with interparticle map shaping (IPMS). The proposed method aims at saving the computational memory in the grid-based RBPF-SLAM while maintaining the mapping accuracy. Unlike conventional RBPF-SLAM in which each particle has its own map of the whole environment, each particle has only a small map of the nearby environment called an individual map in the proposed method. Instead, the map of the remaining large environment is shared by the particles. The part shared by the particles is called a base map. If the individual small maps become reliable enough to trust, they are merged with the base map. To determine when and which part of an individual map should be merged with the base map, we propose two map sharing criteria. Finally, the proposed IPMS RBPF-SLAM is applied to the real-world datasets and benchmark datasets. The experimental results show that our method outperforms conventional methods in terms of map accuracy versus memory consumption.

Using two different environment representations: a detailed one for SLAM, a coarse one for selecting actions for active perception

Nelson, E., Corah, M. & Michael, N., Environment model adaptation for mobile robot exploration,Auton Robot (2018) 42: 257, DOI: 10.1007/s10514-017-9669-2.

In this work, we propose a methodology to adapt a mobile robot’s environment model during exploration as a means of decreasing the computational complexity associated with information metric evaluation and consequently increasing the speed at which the system is able to plan actions and travel through an unknown region given finite computational resources. Recent advances in exploration compute control actions by optimizing information-theoretic metrics on the robot’s map. These metrics are generally computationally expensive to evaluate, limiting the speed at which a robot is able to explore. To reduce computational cost, we propose keeping two representations of the environment: one full resolution representation for planning and collision checking, and another with a coarse resolution for rapidly evaluating the informativeness of planned actions. To generate the coarse representation, we employ the Principal of Relevant Information from rate distortion theory to compress a robot’s occupancy grid map. We then propose a method for selecting a coarse representation that sacrifices a minimal amount of information about expected future sensor measurements using the Information Bottleneck Method. We outline an adaptive strategy that changes the robot’s environment representation in response to its surroundings to maximize the computational efficiency of exploration. On computationally constrained systems, this reduction in complexity enables planning over longer predictive horizons, leading to faster navigation. We simulate and experimentally evaluate mutual information based exploration through cluttered indoor environments with exploration rates that adapt based on environment complexity leading to an order-of-magnitude increase in the maximum rate of exploration in contrast to non-adaptive techniques given the same finite computational resources.

Using sequences of images for loop closure instead of only one

Loukas Bampis, Angelos Amanatiadis, and Antonios Gasteratos, Fast loop-closure detection using visual-word-vectors from image sequences, The International Journal of Robotics Research Vol 37, Issue 1, pp. 62 – 82, DOI: 10.1177/0278364917740639.

In this paper, a novel pipeline for loop-closure detection is proposed. We base our work on a bag of binary feature words and we produce a description vector capable of characterizing a physical scene as a whole. Instead of relying on single camera measurements, the robot’s trajectory is dynamically segmented into image sequences according to its content. The visual word occurrences from each sequence are then combined to create sequence-visual-word-vectors and provide additional information to the matching functionality. In this way, scenes with considerable visual differences are firstly discarded, while the respective image-to-image associations are provided subsequently. With the purpose of further enhancing the system’s performance, a novel temporal consistency filter (trained offline) is also introduced to advance matches that persist over time. Evaluation results prove that the presented method compares favorably with other state-of-the-art techniques, while our algorithm is tested on a tablet device, verifying the computational efficiency of the approach.

Probabilistic SLAM is still the way to go for dynamic environments (according to this paper)

C. Evers and P. A. Naylor, Optimized Self-Localization for SLAM in Dynamic Scenes Using Probability Hypothesis Density Filters, IEEE Transactions on Signal Processing, vol. 66, no. 4, pp. 863-878, DOI: 10.1109/TSP.2017.2775590.

In many applications, sensors that map the positions of objects in unknown environments are installed on dynamic platforms. As measurements are relative to the observer’s sensors, scene mapping requires accurate knowledge of the observer state. However, in practice, observer reports are subject to positioning errors. Simultaneous localization and mapping addresses the joint estimation problem of observer localization and scene mapping. State-of-the-art approaches typically use visual or optical sensors and therefore rely on static beacons in the environment to anchor the observer estimate. However, many applications involving sensors that are not conventionally used for Simultaneous Localization and Mapping (SLAM) are affected by highly dynamic scenes, such that the static world assumption is invalid. This paper proposes a novel approach for dynamic scenes, called GEneralized Motion (GEM) SLAM. Based on probability hypothesis density filters, the proposed approach probabilistically anchors the observer state by fusing observer information inferred from the scene with reports of the observer motion. This paper derives the general, theoretical framework for GEM-SLAM, and shows that it generalizes existing Probability Hypothesis Density (PHD)-based SLAM algorithms. Simulations for a model-specific realization using range-bearing sensors and multiple moving objects highlight that GEM-SLAM achieves significant improvements over three benchmark algorithms.

SLAM based on intervals

Mohamed Mustafa, Alexandru Stancu, Nicolas Delanoue, Eduard Codres, Guaranteed SLAM—An interval approach, Robotics and Autonomous Systems, Volume 100, 2018, Pages 160-170, DOI: 10.1016/j.robot.2017.11.009.

This paper proposes a new approach, interval Simultaneous Localization and Mapping (i-SLAM), which addresses the robotic mapping problem in the context of interval methods, where the robot sensor noise is assumed bounded. With no prior knowledge about the noise distribution or its probability density function, we derive and present necessary conditions to guarantee the map convergence even in the presence of nonlinear observation and motion models. These conditions may require the presence of some anchoring landmarks with known locations. The performance of i-SLAM is compared with the probabilistic counterparts in terms of accuracy and efficiency.

A new approach to SLAM based on KF but without linearization

Feng Tan, Winfried Lohmiller, and Jean-Jacques Slotine, Analytical SLAM without linearization, The International Journal of Robotics Research
Vol 36, Issue 13-14, pp. 1554 – 1578, DOI: 10.1177/0278364917710541.

This paper solves the classical problem of simultaneous localization and mapping (SLAM) in a fashion that avoids linearized approximations altogether. Based on the creation of virtual synthetic measurements, the algorithm uses a linear time-varying Kalman observer, bypassing errors and approximations brought by the linearization process in traditional extended Kalman filtering SLAM. Convergence rates of the algorithm are established using contraction analysis. Different combinations of sensor information can be exploited, such as bearing measurements, range measurements, optical flow, or time-to-contact. SLAM-DUNK, a more advanced version of the algorithm in global coordinates, exploits the conditional independence property of the SLAM problem, decoupling the covariance matrices between different landmarks and reducing computational complexity to O(n). As illustrated in simulations, the proposed algorithm can solve SLAM problems in both 2D and 3D scenarios with guaranteed convergence rates in a full nonlinear context.

Qualitative maps for mobile robots

Jennifer Padgett, Mark Campbell, Probabilistic qualitative mapping for robots, Robotics and Autonomous Systems, Volume 98, 2017, Pages 292-306, DOI: 10.1016/j.robot.2017.09.013.

A probabilistic qualitative relational mapping (PQRM) algorithm is developed to enable robots to robustly map environments using noisy sensor measurements. Qualitative state representations provide soft, relative map information which is robust to metrical errors. In this paper, probabilistic distributions over qualitative states are derived and an algorithm to update the map recursively is developed. Maps are evaluated for convergence and correctness in Monte Carlo simulations. Validation tests are conducted on the New College dataset to evaluate map performance in realistic environments.