Pose SLAM Research Articles

Dual-Layer Path Planning With Pose SLAM for Autonomous Exploration in GPS-Denied Environments

Dual-Layer Path Planning With Pose SLAM for Autonomous Exploration in GPS-Denied Environments

Topological belief space planning for active SLAM with pairwise Gaussian potentials and performance guarantees

Determining a globally optimal solution of belief space planning (BSP) in high-dimensional state spaces directly is computationally expensive, as it involves belief propagation and objective function evaluation for each candidate action. However, many problems of interest, such as active SLAM, exhibit structure that can be harnessed to expedite planning. Also, in order to choose an optimal action, an exact value of the objective function is not required as long as the actions can be sorted in the same way. In this paper we leverage these two key aspects and present the topological belief space planning (t-bsp) concept that uses topological signatures to perform this ranking for information-theoretic cost functions, considering only topologies of factor graphs that correspond to future posterior beliefs. In particular, we propose a highly efficient topological signature based on the von Neumann graph entropy that is a function of graph node degrees and supports an incremental update. We analyze it in the context of active pose SLAM and derive error bounds between the proposed topological signature and the original information-theoretic cost function. These bounds are then used to provide performance guarantees for t-bsp with respect to a given solver of the original information-theoretic BSP problem. Realistic and synthetic simulations demonstrate drastic speed-up of the proposed method with respect to the state-of-the-art methods while retaining the ability to select a near-optimal solution. A proof of concept of t-bsp is given in a small-scale real-world active SLAM experiment.

3D Scene Localization and Mapping Based on Omnidirectional SLAM

The vision SLAM technology is the key technology of robot navigation and unmanned driving. In order to further study the accuracy of multi-view SLAM positioning and mapping applications, this paper combines omnidirectional imaging and SLAM technology, improves the existing technical model in the field of omnidirectional imaging, studies the pose map optimization and SLAM overall adjustment technology based on object constraints and global consistency, and carries out omnidirectional SLAM point cloud reconstruction and 3D mapping. Through the time synchronization experiment of Ladybug 5 Plus and Femtomes Mini2-D-INS, the two positioning results of the system and the GNSS measurement results are compared and analysed.

FT-BSP: Focused Topological Belief Space Planning

At its core, decision making under uncertainty can be regarded as sorting candidate actions according to a certain objective. While finding the optimal solution directly is computationally expensive, other approaches that produce the same ordering of candidate actions, will result in the same selection. With this motivation in mind, we present a computationally efficient approach for the focused belief space planning (BSP) problem, where reducing the uncertainty of only a predefined subset of variables is of interest. Our approach uses topological signatures, defined over the topology induced from factor graph representations of posterior beliefs, to rank candidate actions. In particular, we present two such signatures in the context of information theoretic focused decision making problems. We derive error bounds, with respect to the optimal solution, and prove that one of these signatures converges to the true optimal solution. We also derive a second set of bounds, which is more conservative, but is only a function of topological aspects and can be used online. We introduce the Von Neumann graph entropy for the focused case, which is based on weighted node degrees, and show that it supports incremental update. We then analyze our approach under two different settings, measurement selection and active focused 2D pose SLAM.

Elevation Angle Estimations of Wide-Beam Acoustic Sonar Measurements for Autonomous Underwater Karst Exploration.

This paper proposes a solution for merging the measurements from two perpendicular profiling sonars with different beam-widths, in the context of underwater karst (cave) exploration and mapping. This work is a key step towards the development of a full 6D pose SLAM framework adapted to karst aquifer, where potential water turbidity disqualifies vision-based methods, hence relying on acoustic sonar measurements. Those environments have complex geometries which require 3D sensing. Wide-beam sonars are mandatory to cover previously seen surfaces but do not provide 3D measurements as the elevation angles are unknown. The approach proposed in this paper leverages the narrow-beam sonar measurements to estimate local karst surface with Gaussian process regression. The estimated surface is then further exploited to infer scaled-beta distributions of elevation angles from a wide-beam sonar. The pertinence of the method was validated through experiments on simulated environments. As a result, this approach allows one to benefit from the high coverage provided by wide-beam sonars without the drawback of loosing 3D information.

Neuromorphic Vision Datasets for Pedestrian Detection, Action Recognition, and Fall Detection.

Neuromorphic Vision Datasets for Pedestrian Detection, Action Recognition, and Fall Detection.

The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and SLAM

New vision sensors, such as the dynamic and active-pixel vision sensor (DAVIS), incorporate a conventional global-shutter camera and an event-based sensor in the same pixel array. These sensors have great potential for high-speed robotics and computer vision because they allow us to combine the benefits of conventional cameras with those of event-based sensors: low latency, high temporal resolution, and very high dynamic range. However, new algorithms are required to exploit the sensor characteristics and cope with its unconventional output, which consists of a stream of asynchronous brightness changes (called “events”) and synchronous grayscale frames. For this purpose, we present and release a collection of datasets captured with a DAVIS in a variety of synthetic and real environments, which we hope will motivate research on new algorithms for high-speed and high-dynamic-range robotics and computer-vision applications. In addition to global-shutter intensity images and asynchronous events, we provide inertial measurements and ground-truth camera poses from a motion-capture system. The latter allows comparing the pose accuracy of ego-motion estimation algorithms quantitatively. All the data are released both as standard text files and binary files (i.e. rosbag). This paper provides an overview of the available data and describes a simulator that we release open-source to create synthetic event-camera data.

Potential information fields for mobile robot exploration

We present a decision theoretic approach to mobile robot exploration. The method evaluates the reduction of joint path and map entropy and computes a potential information field in robot configuration space using these joint entropy reduction estimates. The exploration trajectory is computed descending on the gradient of this field. The technique uses Pose SLAM as its estimation backbone. Very efficient kernel convolution mechanisms are used to evaluate entropy reduction for each sensor ray, and for each possible robot orientation, taking frontiers and obstacles into account. In the end, the computation of this field on the entire configuration space is shown to be very efficient. The approach is tested in simulations in a pair of publicly available datasets comparing favorably both in quality of estimates and in execution time against an RRT∗-based search for the nearest frontier and also against a locally optimal exploration strategy.

Information-Based Compact Pose SLAM

Pose SLAM is the variant of simultaneous localization and map building (SLAM) is the variant of SLAM, in which only the robot trajectory is estimated and where landmarks are only used to produce relative constraints between robot poses. To reduce the computational cost of the information filter form of Pose SLAM and, at the same time, to delay inconsistency as much as possible, we introduce an approach that takes into account only highly informative loop-closure links and nonredundant poses. This approach includes constant time procedures to compute the distance between poses, the expected information gain for each potential link, and the exact marginal covariances while moving in open loop, as well as a procedure to recover the state after a loop closure that, in practical situations, scales linearly in terms of both time and memory. Using these procedures, the robot operates most of the time in open loop, and the cost of the loop closure is amortized over long trajectories. This way, the computational bottleneck shifts to data association, which is the search over the set of previously visited poses to determine good candidates for sensor registration. To speed up data association, we introduce a method to search for neighboring poses whose complexity ranges from logarithmic in the usual case to linear in degenerate situations. The method is based on organizing the pose information in a balanced tree whose internal levels are defined using interval arithmetic. The proposed Pose-SLAM approach is validated through simulations, real mapping sessions, and experiments using standard SLAM data sets.