Applying Graph-based SLAM Algorithm in a Simulated Environment

The-state-of-the-art graph optimization method can robustly converge into a solution with least square errors for the graph structure. Nevertheless, when a biased edge (erroneous transformation with over-confident information matrix) exists, the optimal solution can produce the large deviation because of error propagation produced by the biased edge.

arXiv (Cornell University), 2023

In this paper, we propose a solution for graphbased global robot simultaneous localization and mapping (SLAM) using architectural plans. Before the start of the robot operation, the previously available architectural plan of the building is converted into our proposed architectural graph (A-Graph). When the robot starts its operation, it uses its onboard LIDAR and odometry to carry out an online SLAM relying on our situational graph (S-Graph), which includes both, a representation of the environment with multiple levels of abstractions, such as walls or rooms and their relationships, as well as the robot poses with their associated keyframes. Our novel graph-to-graph matching method is used to relate the aforementioned S-Graph and A-Graph, which are aligned and merged, resulting in our novel informed Situational Graph (iS-Graph). Our iS-Graph not only provides graph-based global robot localization, but it extends the graph-based SLAM capabilities of the S-Graph by incorporating into it the prior knowledge of the environment existing in the architectural plan.

Robotics: Science and Systems VII, 2011

This article investigates the problem of Simultaneous Localization and Mapping (SLAM) from the perspective of linear estimation theory. The problem is first formulated in terms of graph embedding: a graph describing robot poses at subsequent instants of time needs be embedded in a three-dimensional space, assuring that the estimated configuration maximizes measurement likelihood. Combining tools belonging to linear estimation and graph theory, a closed-form approximation to the full SLAM problem is proposed, under the assumption that the relative position and the relative orientation measurements are independent. The approach needs no initial guess for optimization and is formally proven to admit solution under the SLAM setup. The resulting estimate can be used as an approximation of the actual nonlinear solution or can be further refined by using it as an initial guess for nonlinear optimization techniques. Finally, the experimental analysis demonstrates that such refinement is often unnecessary, since the linear estimate is already accurate.

2004

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014

SLAM can be viewed as an estimation problem over graphs. It is well known that the topology of each dataset affects the quality of the corresponding optimal estimate. In this paper we present a formal analysis of the impact of graph structure on the reliability of the maximum likelihood estimator. In particular, we show that the number of spanning trees in the graph is closely related to the D-optimality criterion in experimental design. We also reveal that in a special class of linear-Gaussian estimation problems over graphs, the algebraic connectivity is related to the E-optimality design criterion. Furthermore, we explain how the average node degree of the graph is related to the ratio between the minimum of negative log-likelihood achievable and its value at the ground truth. These novel insights give us a deeper understanding of the SLAM problem. Finally we discuss two important applications of our analysis in active measurement selection and graph pruning. The results obtained from simulations and experiments on real data confirm our theoretical findings.

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014

In this work, we provide an in-depth analysis of several recent robust Simultaneous Localization And Mapping (SLAM) back-end techniques that aim to recover the correct graph estimate in the presence of outliers in loop closure constraints. We present a benchmark dataset for evaluation of such methods by augmenting the KITTI Vision Benchmark with ground truth as well as generated loop closure hypotheses and present a detailed analysis of recently proposed robust SLAM methods using this benchmark. We also look into how these methods achieve the desired robustness and what are the implications for the SLAM problem. We discuss the issues involved in using the output of these robust back-ends for tasks such as path planning and how they can be addressed. The problem of robustness needs to be addressed adequately in order to have a complete and reliable solution to the SLAM problem.

Applied Sciences

This paper presents the use of Google’s simultaneous localization and mapping (SLAM) technique, namely Cartographer, and adaptive multistage distance scheduler (AMDS) to improve the processing speed. This approach optimizes the processing speed of SLAM which is known to have performance degradation as the map grows due to a larger scan matcher. In this proposed work, the adaptive method was successfully tested in an actual vehicle to map roads in real time. The AMDS performs a local pose correction by controlling the LiDAR sensor scan range and scan matcher search window with the help of scheduling algorithms. The scheduling algorithms manage the SLAM that swaps between short and long distances during map data collection. As a result, the algorithms efficiently improved performance speed similar to short distance LiDAR scans while maintaining the accuracy of the full distance of LiDAR. By swapping the scan distance of the sensor, and adaptively limiting the search size of the scan m...

KI - Künstliche Intelligenz, 2010

In this paper, we address the problem of lifelong map learning in static environments with mobile robots using the graph-based formulation of the simultaneous localization and mapping problem. The pose graph, which stores the poses of the robot and spatial constraints between them, is the central data structure in graph-based SLAM. The size of the pose graph has a direct influence on the runtime and the memory complexity of the SLAM system and typically grows over time. A robot that performs lifelong mapping in a bounded environment has to limit the memory and computational complexity of its mapping system. We present a novel approach to prune the pose graph so that it only grows when the robot acquires relevant new information about the environment in terms of expected information gain. As a result, our approach scales with the size of the environment and not with the length of the trajectory, which is an important prerequisite for lifelong map learning. The experiments presented in this paper illustrate the properties of our method using real robots.

2006

The concept of autonomy of mobile robots encompasses many areas of knowledge, methods and ultimately algorithms designed for trajectory control, obstacle avoidance, localization, map building, and so forth. Practically, the success of a path planning and navigation mission of an autonomous vehicle depends on the availability of both a sufficiently reliable estimation of the vehicle location, and an accurate representation of the navigation area.

Springer Tracts in Advanced Robotics, 2009

We present an algorithm for SLAM on planar graphs. We assume that a robot moves from node to node on the graph using odometry to measure the distance between consecutive landmark observations. At each node, the robot follows a branch chosen at random, without reporting which branch it follows. A low-level process detects (with some uncertainty) the presence of landmarks, such as corners, branches, and bumps, but only triggers a binary flag for landmark detection (i.e., the robot is oblivious to the details or "appearance" of the landmark). Under uncertainties of the robot's odometry, landmark detection, and the current landmark position of the robot, we present an E-M-based SLAM algorithm for two cases: (1) known, arbitrary topology with unknown edge lengths and (2) unknown topology, but restricted to "elementary" 1-and 2-cycle graphs. In the latter case, the algorithm (flexibly and reversibly) closes loops and allows for dynamic environments (adding and deleting nodes).