Bearing only cooperative localization has been used successfully on aerial and ground vehicles. In this paper we present an extension of the approach to the underwater domain. The focus is on adapting the technique to handle the challenging visibility conditions underwater. Furthermore, data from inertial, magnetic, and depth sensors are utilized to improve the robustness of the estimation. In addition to robotic applications, the presented technique can be used for cave mapping and for marine archeology surveying, both by human divers. Experimental results from different environments, including a fresh water, low visibility, lake in South Carolina; a cavern in Florida; and coral reefs in Barbados during the day and during the night, validate the robustness and the accuracy of the proposed approach.
The concept of CL was first introduced by Kurazume and Hirosi @cite_26 , and the term was first used by Rekleitis al @cite_21 . At a recent count, more than 100 papers have been published since then covering many aspects. The CL approach has been used both in 2D and 3D, for localization @cite_24 and also for SLAM @cite_4 . Using images @cite_1 @cite_6 , LIDAR @cite_36 @cite_35 , and sonar @cite_15 . The problem of CL with anonymous robots'' was presented by Franchi al @cite_18 . More recently, Martinelli and Renzaglia @cite_10 provided the fundamental equations for fusing CL estimates with inertial data.
@cite_35 @cite_18 @cite_26 @cite_4 @cite_36 @cite_21 @cite_1 @cite_6 @cite_24 @cite_15 @cite_10
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In this paper we investigate the problem of Simultaneous Localization and Mapping (SLAM) for a multi robot system. Relaxing some assumptions that characterize related work we propose an application of Rao-Blackwellized Particle Filters (RBPF) for the purpose of cooperatively estimating SLAM posterior. We consider a realistic setup in which the robots start from unknown initial poses (relative locations are unknown too), and travel in the environment in order to build a shared representation of the latter. The robots are required to exchange a small amount of information only when a rendezvous event occurs and to measure relative poses during the meeting. As a consequence the approach also applies when using an unreliable wireless channel or short range communication technologies (bluetooth, RFId, etc.). Moreover it allows to take into account the uncertainty in relative pose measurements. The proposed technique, which constitutes a distributed solution to the multi robot SLAM problem, is further validated through simulations and experimental tests. This paper describes an on-line algorithm for multi-robot simultaneous localization and mapping (SLAM). The starting point is the single-robot Rao-Blackwellized particle filter described by , and three key generalizations are made. First, the particle filter is extended to handle multi-robot SLAM problems in which the initial pose of the robots is known (such as occurs when all robots start from the same location). Second, an approximation is introduced to solve the more general problem in which the initial pose of robots is not known a priori (such as occurs when the robots start from widely separated locations). In this latter case, it is assumed that pairs of robots will eventually encounter one another, thereby determining their relative pose. This relative attitude is used to initialize the filter, and subsequent observations from both robots are combined into a common map. Third and finally, a method is introduced to integrate observations collected prior to the first robot encounter, using the notion of a virtual robot travelling backwards in time. This novel approach allows one to integrate all data from all robots into a single common map. This paper proposes a pose-based algorithm to solve the full Simultaneous Localization And Mapping (SLAM) problem for an Autonomous Underwater Vehicle (AUV), navigating in an unknown and possibly unstructured environment. A probabilistic scan matching technique using range scans gathered from a Mechanical Scanning Imaging Sonar (MSIS) is used together with the robot dead-reckoning displacements. The proposed method utilizes two Extended Kalman Filters (EKFs). The first, estimates the local path traveled by the robot while forming the scan as well as its uncertainty, providing position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augmented state EKF that estimates and keeps the registered scans poses. The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. Also, a method of estimating the uncertainty of the scan matching estimation is provided. The algorithm has been tested on an AUV guided along a 600 m path within a marina environment, showing the viability of the proposed approach. The Simultaneous Localization And Mapping (SLAM) problem has been well studied in the robotics community, especially using mono, stereo cameras or depth sensors. 3D depth sensors, such as Velodyne LiDAR, have proved in the last 10 years to be very useful to perceive the environment in autonomous driving, but few methods exist that directly use these 3D data for odometry. We present a new low-drift SLAM algorithm based only on 3D LiDAR data. Our method relies on a scan-to-model matching framework. We first have a specific sampling strategy based on the LiDAR scans. We then define our model as the previous localized LiDAR sweeps and use the Implicit Moving Least Squares (IMLS) surface representation. We show experiments with the Velodyne HDL32 with only 0.40 drift over a 4 km acquisition without any loop closure (i.e., 16 m drift after 4 km). We tested our solution on the KITTI benchmark with a Velodyne HDL64 and ranked among the best methods (against mono, stereo and LiDAR methods) with a global drift of only 0.69 .