A SLAM Map Restoration Algorithm Based on Submaps and an Undirected Connected Graph

Abstract

Many visual simultaneous localization and mapping (SLAM) systems have been shown to be accurate and robust, and have real-time performance capabilities on both indoor and ground datasets. However, these methods can be problematic when dealing with aerial frames captured by a camera mounted on an unmanned aerial vehicle (UAV) because the flight height of the UAV can be difficult to control and is easily affected by the environment. For example, the UAV may be shaken or experience a rapid drop in height due to sudden strong wind, which may in turn lead to lost tracking. What is more, when photographing a large area, the UAV flight path is usually planned in advance and the UAV does not generally return to the previously covered areas, so if the tracking fails during the flight, many areas of the map will be missing. To cope with the case of lost tracking, we present a method of reconstructing a complete global map of UAV datasets by sequentially merging the submaps via the corresponding undirected connected graph. Specifically, submaps are repeatedly generated, from the initialization process to the place where the tracking is lost, and a corresponding undirected connected graph is built by considering these submaps as nodes and the common map points within two submaps as edges. The common map points are then determined by the bag-of-words (BoW) method, and the submaps are merged if they are found to be connected with the online map in the undirect connected graph. To demonstrate the performance of the proposed method, we first investigated the performance on a UAV dataset, and the experimental results showed that, in the case of several tracking failures, the integrity of the mapping was significantly better than that of the current mainstream SLAM method. We also tested the proposed method on both ground and indoor datasets, where it again showed a superior performance.

A Comprehensive Survey of Visual SLAM Algorithms

“…ORB-SLAM2 considers the monocular, stereo and RGB-D approaches, and implements global optimization and loop closure techniques. Nonetheless, the tracking failure situation may lead to a lost state if the system does not recognize a high-similarity frame [45]. In addition, this method needs to acquire the images with the same frame rate as it processes them, which makes real-time operation in embedded platforms difficult [46].…”

Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

“…To recognize already visited places in landmarks, compact point cloud descriptors are compared between two matching points [118]. LiDAR cameras are used for submap matching as LiDAR scans and point-clouds can be clustered into submaps [119]. LiDAR has sparse, high precision depth data while cameras have dense, but low precision depth data [105].…”

“…The UAV trajectory can be represented by weighted samples, and a map is computed analytically over a smaller trajectory in the submap, where each landmark is independent [119]. For m landmark locations, n filters compute UAV position distribution.…”

“…Out of these K classes, K 1 , K 2 , • • • , K n sequences of UAV odometry dataset assists in estimating the UAV trajectory. During this sequence, the 2D sensor may not detect any obstacle and predict safe trajectory, with increments in the translational error for all subsequences [119]. The error score is the mean of all sub-sequence errors [47].…”

“…• A large point-cloud depends on the non-linearity of the observations and the linear approximation is valid in a small range [70,80]. For multiple submaps [119], the EKF filter is linearized at every qn−1 that leads to nonlinear error in EKF [147]. Estimation of spatial uncertainty in the UAV trajectory uses EKF to estimate the mean and covariance of the states [74].…”