This article presents a comparative analysis of g2o and Ceres solvers in enhancing scan matching performance within the Cartographer framework. Cartographer, a widely-used library for Simultaneous Localization and Mapping (SLAM), relies on optimization algorithms to refine pose estimates and improve map accuracy. The research aims to evaluate the performance, efficiency, and accuracy of the g2o solver in comparison to the Ceres solver, which is the default in Cartographer. In our experiments comparing Ceres and g2o within Cartographer, Ceres outperformed g2o in terms of speed, convergence efficiency, and overall map clarity. Ceres required fewer iterations and less time to converge, producing more accurate and well-defined maps, especially in real-world mapping scenarios with the AgileX LIMO robot. However, g2o excelled in localized obstacle detection, highlighting its value in specific situations.

Joint optimization of poses and features has been extensively studied and demonstrated to yield more accurate results in feature-based SLAM problems. However, research on jointly optimizing poses and non-feature-based maps remains limited. Occupancy maps are widely used non-feature-based environment representations because they effectively classify spaces into obstacles, free areas, and unknown regions, providing robots with spatial information for various tasks. In this paper, we propose Occupancy-SLAM, a novel optimization-based SLAM method that enables the joint optimization of robot trajectory and the occupancy map through a parameterized map representation. The key novelty lies in optimizing both robot poses and occupancy values at different cell vertices simultaneously, a significant departure from existing methods where the robot poses need to be optimized first before the map can be estimated. Evaluations using simulations and practical 2D laser datasets demonstrate that the proposed approach can robustly obtain more accurate robot trajectories and occupancy maps than state-of-the-art techniques with comparable computational time. Preliminary results in the 3D case further confirm the potential of the proposed method in practical 3D applications, achieving more accurate results than existing methods.

Optimizing robot poses and the map simultaneously has been shown to provide more accurate SLAM results. However, for non-feature based SLAM approaches, directly optimizing all the robot poses and the whole map will greatly increase the computational cost, making SLAM problems difficult to solve in large-scale environments. To solve the 2D non-feature based SLAM problem in large-scale environments more accurately and efficiently, we propose the grid-based submap joining method. Specifically, we first formulate the 2D grid-based submap joining problem as a non-linear least squares (NLLS) form to optimize the global occupancy map and local submap frames simultaneously. We then prove that in solving the NLLS problem using Gauss-Newton (GN) method, the increments of the poses in each iteration are independent of the occupancy values of the global occupancy map. Based on this property, we propose a poseonly GN algorithm equivalent to full GN method to solve the NLLS problem. The proposed submap joining algorithm is very efficient due to the independent property and the pose-only solution. Evaluations using simulations and publicly available practical 2D laser datasets confirm the outperformance of our proposed method compared to the state-of-the-art methods in terms of efficiency and accuracy, as well as the ability to solve the grid-based SLAM problem in very large-scale environments.

Simultaneous Localization and Mapping (SLAM) is a key component of autonomous systems operating in environments that require a consistent map for reliable localization. SLAM has been a widely studied topic for decades with most of the solutions being camera or LiDAR based. Early LiDAR-based approaches primarily relied on 2D data, whereas more recent frameworks use 3D data. In this work, we survey recent 3D LiDAR-based Graph-SLAM methods in urban environments, aiming to compare their strengths, weaknesses, and limitations. Additionally, we evaluate their robustness regarding the LiDAR resolution namely 64 $vs$ 128 channels. Regarding SLAM methods, we evaluate SC-LeGO-LOAM, SC-LIO-SAM, Cartographer, and HDL-Graph on real-world urban environments using the KITTI odometry dataset (a LiDAR with 64-channels only) and a new dataset (AUTONOMOS-LABS). The latter dataset, collected using instrumented vehicles driving in Berlin suburban area, comprises both 64 and 128 LiDARs. The experimental results are reported in terms of quantitative `metrics' and complemented by qualitative maps.

In this paper, we propose an optimization based SLAM approach to simultaneously optimize the robot trajectory and the occupancy map using 2D laser scans (and odometry) information. The key novelty is that the robot poses and the occupancy map are optimized together, which is significantly different from existing occupancy mapping strategies where the robot poses need to be obtained first before the map can be estimated. In our formulation, the map is represented as a continuous occupancy map where each 2D point in the environment has a corresponding evidence value. The Occupancy-SLAM problem is formulated as an optimization problem where the variables include all the robot poses and the occupancy values at the selected discrete grid cell nodes. We propose a variation of Gauss-Newton method to solve this new formulated problem, obtaining the optimized occupancy map and robot trajectory together with their uncertainties. Our algorithm is an offline approach since it is based on batch optimization and the number of variables involved is large. Evaluations using simulations and publicly available practical 2D laser datasets demonstrate that the proposed approach can estimate the maps and robot trajectories more accurately than the state-of-the-art techniques, when a relatively accurate initial guess is provided to our algorithm. The video shows the convergence process of the proposed Occupancy-SLAM and comparison of results to Cartographer can be found at \url{https://youtu.be/4oLyVEUC4iY}.

Despite the number of works published in recent years, vehicle localization remains an open, challenging problem. While map-based localization and SLAM algorithms are getting better and better, they remain a single point of failure in typical localization pipelines. This paper proposes a modular localization architecture that fuses sensor measurements with the outputs of off-the-shelf localization algorithms. The fusion filter estimates model uncertainties to improve odometry in case absolute pose measurements are lost entirely. The architecture is validated experimentally on a real robot navigating autonomously proving a reduction of the position error of more than 90% with respect to the odometrical estimate without uncertainty estimation in a two-minute navigation period without position measurements.

Localization approaches for autonomous racing, such as pose-graph based Simultaneous Localization and Mapping (SLAM) [1] and Monte-Carlo Localization (MCL)-based (also called Particle Filtering, or PF) methods [2-4] depend on both exteroceptive and proprioceptive inputs. For example, LiDAR sensors offer range measurements for exteroceptive sensing, enabling the robot to perceive its environment. In contrast, proprioceptive measurements provide insight into the robot's internal states, processing signals from IMUs and wheel-odometry. Consequently, SLAM algorithms can map environments while localizing the robot. On the other hand, MCL-based techniques, relying on both sensing modalities and a pre-existing map, Figure 1: Comparison of poses generated by diff-drive [2] and solely determine the robot localization using MCL [3, 4].

We study algorithms for detecting and including glass objects in an optimization-based Simultaneous Localization and Mapping (SLAM) algorithm in this work. When LiDAR data is the primary exteroceptive sensory input, glass objects are not correctly registered. This occurs as the incident light primarily passes through the glass objects or reflects away from the source, resulting in inaccurate range measurements for glass surfaces. Consequently, the localization and mapping performance is impacted, thereby rendering navigation in such environments unreliable. Optimization-based SLAM solutions, which are also referred to as Graph SLAM, are widely regarded as state of the art. In this paper, we utilize a simple and computationally inexpensive glass detection scheme for detecting glass objects and present the methodology to incorporate the identified objects into the occupancy grid maintained by such an algorithm (Google Cartographer). We develop both local (submap level) and global algorithms for achieving the objective mentioned above and compare the maps produced by our method with those produced by an existing algorithm that utilizes particle filter based SLAM.

Quadrupeds are highly versatile robots that can traverse over difficult terrain that wheeled mobile robots are unable to. This flexibility makes quadrupeds appealing for various applications, such as inspection, surveying construction sites, and search-and-rescue. However, to effectively perform these tasks autonomously, quadrupeds, as with other mobile robots, require a form of perception that will enable them to localize when placed in an environment without a priori knowledge. For robots to know its location in the environment, it must localize against a predefined map, but a robot can only create a map based on its known location. To solve this chicken-and-egg problem, simultaneous localization and mapping, or SLAM, is the standard approach used for mobile robots by optimizing for the robot's location and map simultaneously. The estimated poses and map from SLAM algorithms can then be used for downstream tasks such as facilitating controllers depending on the terrain or planning in navigation. Despite the recent developments in both quadruped robotics and in SLAM research, there has yet to be an open-source package that is specifically designed for high performing SLAM on quadrupeds.

Recent interest towards autonomous navigation and exploration robots for indoor applications has spurred research into indoor Simultaneous Localization and Mapping (SLAM) robot systems. While most of these SLAM systems use Visual and LiDAR sensors in tandem with an odometry sensor, these odometry sensors drift over time. To combat this drift, Visual SLAM systems deploy compute and memory intensive search algorithms to detect `Loop Closures', which make the trajectory estimate globally consistent. To circumvent these resource (compute and memory) intensive algorithms, we present ViWiD, which integrates WiFi and Visual sensors in a dual-layered system. This dual-layered approach separates the tasks of local and global trajectory estimation making ViWiD resource efficient while achieving on-par or better performance to state-of-the-art Visual SLAM. We demonstrate ViWiD's performance on four datasets, covering over 1500 m of traversed path and show 4.3x and 4x reduction in compute and memory consumption respectively compared to state-of-the-art Visual and Lidar SLAM systems with on par SLAM performance.