Towards Model-Free SLAM Using a Single Laser Range Scanner for Helicopter MAV (original) (raw)

Mapping with Micro Aerial Vehicles by Registration of Sparse 3D Laser Scans

Micro aerial vehicles (MAVs) pose speci c constraints on onboard sensing, mainly limited payload and limited processing power. For accurate 3D mapping even in GPS denied environments, we have designed a light-weight 3D laser scanner speci cally for the application on MAVs. Similar to other custom-built 3D laser scanners composed of a rotating 2D laser range nder, it exhibits di erent point densities within and between individual scan lines. When rotated fast, such non-uniform point densities in uence neighborhood searches which in turn may negatively a ect local feature estimation and scan registration. We present a complete pipeline for 3D mapping including pair-wise registration and global alignment of 3D scans acquired in- ight. For registration, we extend a state-of-the-art registration algorithm to include topological information from approximate surface reconstructions. For global alignment, we use a graph-based approach making use of the same error metric and iteratively re ne the complete vehicle trajectory. In experiments, we show that our approach can compensate for the e ects caused by different point densities up to very low angular resolutions and that we can build accurate and consistent 3D maps in- ight with a micro aerial vehicle.

Joint 3D Laser and Visual Fiducial Marker based SLAM for a Micro Aerial Vehicle

Laser scanners have been proven to provide reliable and highly precise environment perception for micro aerial vehicles (MAV). This oftentimes makes them the first choice for tasks like obstacle avoidance, close inspection of structures, self-localization, and mapping. However, artificial environments may pose problems if the scene is self-similar or symmetric and, hence, localization becomes ambiguous if only relying on distance measurements (e.g., when flying along a parallel aisle).

Registration of Non-Uniform Density 3D Laser Scans for Mapping with Micro Aerial Vehicles

Micro aerial vehicles (MAVs) pose specific constraints on onboard sensing, mainly limited payload and limited processing power. For accurate 3D mapping even in GPS-denied environments, we have designed a lightweight 3D laser scanner specifically for the application on MAVs. Similar to other custom-built 3D laser scanners composed of a rotating 2D laser range finder, it exhibits different point densities within and between individual scan lines. When rotated fast, such non-uniform point densities influence neighborhood searches which in turn may negatively affect local feature estimation and scan registration. We present a complete pipeline for 3D mapping including pair-wise registration and global alignment of such non-uniform density 3D point clouds acquired in-flight. For registration, we extend a state-of-the-art registration algorithm to include topological information from approximate surface reconstructions. For global alignment, we use a graph-based approach making use of the same error metric and iteratively refine the complete vehicle trajectory. In experiments, we show that our approach can compensate for the effects caused by different point densities up to very low angular resolutions and that we can build accurate and consistent 3D maps in-flight with a micro aerial vehicle.

3-D site mapping with the CMU autonomous helicopter

Proceedings of the 5th International Conference on …, 1998

This paper describes a scanning laser rangefinder developed for integration with the Carnegie Mellon University autonomous helicopter. The combination of an unmanned, autonomous helicopter with a 3-D scanning laser rangefinder has many potential applications; such as terrain modeling or structure inspection. To achieve high accuracy (10 cm) in each 3-D measurement, careful attention must be paid to minimizing errors, in

Interactive SLAM using Laser and Advanced Sonar

Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005

This paper presents a novel approach to mapping for mobile robots that exploits user interaction to semiautonomously create a labelled map of the environment. The robot autonomously follows the user and is provided with a verbal commentary on the current location with phrases such as "Robot, we are in the office". At the same time, a metric feature map is generated using fusion of laser and advanced sonar measurements in a Kalman filter based SLAM framework, which is later used for localization. When mapping is complete, the robot generates an occupancy grid for use in global task planning. The occupancy grid is created using a novel laser scan registration scheme that relies on storing the path of the robot along with associated local SLAM features during mapping, and later recovering the path by matching the associated local features to the final SLAM map. The occupancy grid is segmented into labelled rooms using an algorithm based on watershed segmentation and integration of the verbal commentary. Experimental results demonstrate our mobile robot creating SLAM and segmented occupancy grid maps of rooms along a 70 metre corridor, and then using these maps to navigate between rooms.

Towards Autonomous MAV Exploration in Cluttered Indoor and Outdoor Environments

Measurement Unit (IMU), a Core2Duo board, an OMAP3530 processor, and an FPGA board. Stereo images are processed on the FPGA by the Semi-Global Matching algorithm with a resolution of 0.5 MPixel at a rate of 14.6 Hz. Keyframe-based stereo odometry is fused with IMU data compensating time delays of about 250 ms that are induced by the vision pipeline. The system state estimate is used for control and on-board 3D mapping. An operator can set waypoints in the map, while the quadrotor autonomously plans its path avoiding obstacles. We show experiments with the quadrotor flying from inside a building to the outside and vice versa, flying through a window and a door respectively. A video of the experiments is part of this work.

DLL: Direct LIDAR Localization. A map-based localization approach for aerial robots

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021

This paper presents DLL, a fast direct map-based localization technique using 3D LIDAR for its application to aerial robots. DLL implements a point cloud to map registration based on non-linear optimization of the distance of the points and the map, thus not requiring features, neither point correspondences. Given an initial pose, the method is able to track the pose of the robot by refining the predicted pose from odometry. Through benchmarks using real datasets and simulations, we show how the method performs much better than Monte-Carlo localization methods and achieves comparable precision to other optimization-based approaches but running one order of magnitude faster. The method is also robust under odometric errors. The approach has been implemented under the Robot Operating System (ROS), and it is publicly available.

A Novel Navigation Algorithm for Mapping Indoor Environments with a Quadrotor

Hittite Journal of Science & Engineering, 2020

R ecently, quadrotors gained popularity due to their high maneuverability, cost and vertical takeoff/landing capabilities. Nevertheless, they also have disadvantages such as they have a limited flight time and small payload capabilities. In addition to these, a major part of the energy of a quadrotor is spent against gravity for hovering. Still, they are one of the most adopted air vehicles for commercial and research purposes. Most of the time UAV's (Unmanned Aerial Vehicles) are used in outdoor applications such as surveillance, search/rescue and patrolling. Recently, UAV's started to find uses in indoor environments. Material handling in manufacturing and inspection in harsh environments are to name a few[1] of these applications. One of the most promising applications is to utilize them in urban relief and disaster operations where a UAV moves autonomously avoiding obstacles in GPS-denied buildings to help human operators for rescue operations. In order to navigate in an indoor environment, a map of the environment is needed. However, in most of the cases either the map is unknown or some partial information about the environment is available. Indoor mapping process can be performed by using seve-Article History:

MVCSLAM: Mono-Vision Corner SLAM for Autonomous Micro-Helicopters in GPS Denied Environments

AIAA Guidance, Navigation and Control Conference and Exhibit, 2008

We present a real-time vision navigation and ranging method (VINAR) for the purpose of Simultaneous Localization and Mapping (SLAM) using monocular vision. Our navigation strategy assumes a GPS denied unknown environment, whose indoor architecture is represented via corner based feature points obtained through a monocular camera. We experiment on a case study mission of vision based SLAM through a conventional maze of corridors in a large building with an autonomous Micro Aerial Vehicle (MAV). We propose a method for gathering useful landmarks from a monocular camera for SLAM use. We make use of the corners by exploiting the architectural features of the manmade indoors.