Laser-Based Online Sliding-Window Approach for UAV Loop-Closure Detection in Urban Environments

Online loop-closure detection serves as an essential task for Unmanned Aerial Vehicles (UAVs) equipped with laser scanners. Due to the inherent errors in UAVs' pose estimation, a 3-D reconstruction algorithm is adopted to perform 3-D map building, which establishes probabilistic models of the system according to the assumption of errors. To meet the demand of online loop-closure detection using sequential 2-D laser data, a robust ISW-NDT (incremental sliding-window-based NDT) approach is proposed, which compares the appearance similarity between two scans by sliding a window with fixed size. Compared with the conventional 3-D NDT approach, the proposed loop-closure detection algorithm is capable of providing superior performance in large-scale outdoor environments, achieving higher recall rate at 100% precision and ensuring successful online implementation. Experimental results show the validity and robustness of the proposed method.

Place recognition based on matching of planar surfaces and line segments

This paper considers the potential of using three-dimensional (3D) planar surfaces and line segments detected in depth images for place recognition. A place recognition method is presented that is based on matching sets of surface and line features extracted from depth images provided by a 3D camera to features of the same type contained in a previously created environment model. The considered environment model consists of a set of local models representing particular locations in the modeled environment. Each local model consists of planar surface segments and line segments representing the edges of objects in the environment. The presented method is designed for indoor and urban environments. A computationally efficient pose hypothesis generation approach is proposed that ranks the features according to their potential contribution to the pose information, thereby reducing the time needed for obtaining accurate pose estimation. Furthermore, a robust probabilistic method for selecting the best pose hypothesis is proposed that allows matching of partially overlapping point clouds with gross outliers. The proposed approach is experimentally tested on a benchmark dataset containing depth images acquired in the indoor environment with changes in lighting conditions and the presence of moving objects. A comparison of the proposed method to FAB-MAP and DLoopDetector is reported.

A Robust Visual-Feature-Extraction Method for Simultaneous Localization and Mapping in Public Outdoor Environment

We describe a new feature extraction method based on the geometric structure of matched local feature points that extracts robust features from an image sequence and performs satisfactorily in highly dynamic environments. Our proposed method is more accurate than other such methods in appearance-only simultaneous localization and mapping (SLAM). Compared to position-invariant robust features [1], it is also more suitable for low-cost, single lens cameras with narrow fields of view. Testing our method in an outdoor environment at Shibuya Station. We captured images using a conventional hand-held single-lens video camera. These environments of experiments are public environments without any planned landmarks. Results have shown that the proposed method accurately obtains matches for two visual-feature sets and that stable, accurate SLAM is achieved in dynamic public environments.

Solution to the SLAM problem in low dynamic environments using a pose graph and an RGB-D sensor

In this study, we propose a solution to the simultaneous localization and mapping (SLAM) problem in low dynamic environments by using a pose graph and an RGB-D (red-green-blue depth) sensor. The low dynamic environments refer to situations in which the positions of objects change over long intervals. Therefore, in the low dynamic environments, robots have difficulty recognizing the repositioning of objects unlike in highly dynamic environments in which relatively fast-moving objects can be detected using a variety of moving object detection algorithms. The changes in the environments then cause groups of false loop closing when the same moved objects are observed for a while, which means that conventional SLAM algorithms produce incorrect results. To address this problem, we propose a novel SLAM method that handles low dynamic environments. The proposed method uses a pose graph structure and an RGB-D sensor. First, to prune the falsely grouped constraints efficiently, nodes of the graph, that represent robot poses, are grouped according to the grouping rules with noise covariances. Next, false constraints of the pose graph are pruned according to an error metric based on the grouped nodes. The pose graph structure is reoptimized after eliminating the false information, and the corrected localization and mapping results are obtained. The performance of the method was validated in real experiments using a mobile robot system.

Vision-based topological mapping and localization by means of local invariant features and map refinement

We propose an appearance-based approach for topological visual mapping and localization using local invariant features. To optimize running times, matchings between the current image and previously visited places are determined using an index based on a set of randomized kd-trees. We use a discrete Bayes filter for predicting loop candidates, whose observation model is a novel approach based on an efficient matching scheme between features. In order to avoid redundant information in the resulting maps, we also present a map refinement framework, which takes into account the visual information stored in the map for refining the final topology of the environment. These refined maps save storage space and improve the execution times of localizations tasks. The approach is validated using image sequences from several environments and compared with the state-of-the-art FAB-MAP 2.0 algorithm.

An Efficient Ceiling-view SLAM Using Relational Constraints Between Landmarks

In this paper, we present a new indoor 'simultaneous localization and mapping‘ (SLAM) technique based on an upward-looking ceiling camera. Adapted from our previous work [ 17 ], the proposed method employs sparsely-distributed line and point landmarks in an indoor environment to aid with data association and reduce extended Kalman filter computation as compared with earlier techniques. Further, the proposed method exploits geometric relationships between the two types of landmarks to provide added information about the environment. This geometric information is measured with an upward-looking ceiling camera and is used as a constraint in Kalman filtering. The performance of the proposed ceiling-view (CV) SLAM is demonstrated through simulations and experiments. The proposed method performs localization and mapping more accurately than those methods that use the two types of landmarks without taking into account their relative geometries.

Fuzzy ART-based place recognition for visual loop closure detection

The automatic place recognition problem is one of the key challenges in SLAM approaches for loop closure detection. Most of the appearance-based solutions to this problem share the idea of image feature extraction, memorization, and matching search. The weakness of these solutions is the storage and computational costs which increase drastically with the environment size. In this regard, the major constraints to overcome are the required visual information storage and the complexity of similarity computation. In this paper, a novel formulation is proposed that allows the computation time reduction while no visual information are stored and matched explicitly. The proposed solution relies on the incremental building of a bio-inspired visual memory using a Fuzzy ART network. This network considers the properties discovered in primate brain. The performance evaluation of the proposed method has been conducted using two datasets representing different large scale outdoor environments. The method has been compared with RatSLAM and FAB-MAP approaches and has demonstrated a decreased time and storage costs with broadly comparable precision recall performance.

CAT-SLAM: probabilistic localisation and mapping using a continuous appearance-based trajectory

This paper describes a new system, dubbed Continuous Appearance-based Trajectory Simultaneous Localisation and Mapping (CAT-SLAM), which augments sequential appearance-based place recognition with local metric pose filtering to improve the frequency and reliability of appearance-based loop closure. As in other approaches to appearance-based mapping, loop closure is performed without calculating global feature geometry or performing 3D map construction. Loop-closure filtering uses a probabilistic distribution of possible loop closures along the robot’s previous trajectory, which is represented by a linked list of previously visited locations linked by odometric information. Sequential appearance-based place recognition and local metric pose filtering are evaluated simultaneously using a Rao–Blackwellised particle filter, which weights particles based on appearance matching over sequential frames and the similarity of robot motion along the trajectory. The particle filter explicitly models both the likelihood of revisiting previous locations and exploring new locations. A modified resampling scheme counters particle deprivation and allows loop-closure updates to be performed in constant time for a given environment. We compare the performance of CAT-SLAM with FAB-MAP (a state-of-the-art appearance-only SLAM algorithm) using multiple real-world datasets, demonstrating an increase in the number of correct loop closures detected by CAT-SLAM.

A pure vision-based topological SLAM system

In this paper, we present a feasible solution to the problem of autonomous navigation in initially unknown environments using a pure vision-based approach. The mobile robot performs range sensing with a unique omnidirectional stereovision system, estimates its motion using visual odometry and detects loop closures via a place recognition system as it performs topological map building and localization concurrently. Owing to the importance of performing loop closing regularly, the mobile robot is equipped with an active loop closure detection and validation system that assists it to return to target loop closing locations, validates ambiguous loop closures and provides it with the ability to overturn the decision of an incorrectly committed loop closure. A refined incremental probabilistic framework for an appearance-based place recognition system is fully described and the final system is validated in multiple experiments conducted in indoor, semi-outdoor and outdoor environments. Lastly, the performance of the probabilistic framework is compared with the rank-based framework with additional experiments conducted in the semi-autonomous mode, where the mobile robot, provided with a priori information in the form of a topological map that is built in a separate occasion in an offline manner, is required to reach its target destination.