Antonio J. Reina

Development of ALACRANE: A Mobile Robotic Assistance for Exploration and Rescue Missions

The paper presents ALACRANE, a new mobile robot assistant for exploration and rescue missions with dexterous load manipulation capability. ALACRANE consists of a tracked vehicle with a 4-DOF articulated arm, whose end-effector is an independent pair of 3-DOF manipulators (LR-Arms) plus a common rotation on the main arm wrist. All actuators are hydraulic in order to provide a high power-to-size ratio for both traction and manipulation. The system is equipped with CCD and IR cameras and a 3D-laser scanner for victim detection and environment perception. Three operation modes have been envisaged for the robot: navigation, main arm positioning, and LR-Arms operation. The control architecture provides different levels of autonomy and tele-operation for each mode. Preliminary tests with the actual system are presented.

A two-stage mobile robot localization method by overlapping segment-based maps☆

This paper presents a new method for accurately estimating the pose (position and orientation) of a mobile robot by registering a segment-based local map observed from the current robot pose and a global map. The method works in a two-stage procedure. First, the orientation is determined by aligning the local and global map through a voting process based on a generalized Hough transform. Second, it uses a coarse-to-fine approach for selecting candidate positions and a weighted voting scheme to determine the degree of overlap of the two maps at each of these poses. Unlike other methods previously proposed, this approach allows us to uncouple the problem of estimating the robot orientation and the robot position which may be useful for some applications. In addition it can manage environments described by many (possibly short) segments. This paper presents some experimental results based on our mobile robot RAM-2 that show the accuracy and the robustness of the proposed method even for poor quality maps and large dead-reckoning errors.

Fast range-independent spherical subsampling of 3D laser scanner points and data reduction performance evaluation for scene registration

Three-dimensional laser range-finders are increasingly being incorporated into applications, such as mobile robotics, that require real-time registration of scene data. However, the computational costs of adaptive range-dependent data selection and point cloud matching grow significantly with the number of points. Therefore, fast range-independent subsampling by uniform or random data reduction is usually performed at a preprocessing step. The paper proposes a new range-independent subsampling algorithm that is more effective for the widely used spherical scanning mechanism. As this type of device measures the ranges by composition of two rotations, it samples certain directions with a higher density, which can distort the registration optimization process. The proposed solution uses sensor characteristics to equalize the measure-direction density of the reduced point cloud. The paper also addresses performance assessment of subsampling methods by contributing three benchmark criteria that do not rely on a particular registration technique: one considers the ground truth transformation between two scans, and the other two are based on the analysis of a single scan. The advantages of spherical subsampling are analyzed through a comparison of range-independent methods and a simple range-dependent one with real scans from three representative scenes (urban, natural, and indoors).

Automation of the Arm-Aided Climbing Maneuver for Tracked Mobile Manipulators

Surmounting terrain elevations, such as terraces, is useful to increase the reach of mobile robots operating in disaster areas, construction sites, and natural environments. This paper proposes an autonomous climbing maneuver for tracked mobile manipulators with the help of the onboard arm. The solution includes a fast 3-D scan processing method to estimate a simple set of geometric features for the ascent: three lines that correspond to the low and high edges, and the maximum inclination axis. Furthermore, terraces are classified depending on whether they are reachable through a slope or an abrupt step. In the proposed maneuver, the arm is employed both for shifting the center of gravity of the robot and as an extra limb that can be pushed against the ground. Feedback during climbing can be obtained through an inertial measurement unit, joint absolute encoders, and pressure sensors. Experimental results are presented for terraces of both kinds on rough terrain with the hydraulic mobile manipulator Alacrane.

Characterization of a radial laser scanner for mobile robot navigation

A radial laser scanner is a device that provides distances to the surrounding objects by scanning the environment in a plane (usually parallel to the ground). This paper is concerned with the calibration of one of such a sensor, called the Explorer. In particular we present a probabilistic sensor model that considers the sensor readings to be affected by Gaussian noise as well as truncated by the sensor resolution. We also describe some experiments aimed to characterize the range measurements against the operating time, different target materials, beam incidence angle, etc. A brief analysis of the angular error is also presented.

3D registration of laser range scenes by coincidence of coarse binary cubes

Scene registration of a pair of three-dimensional (3D) range images is a 6D optimization problem usually required in mobile robotics. This problem is different from object registration, since all scan directions and depths may contain relevant data, and because farther regions are sampled with lower densities. The paper proposes an efficient scene matching method based on the concept of coarse binary cubes. An integer objective function is defined as the number of coincident cubes between both scans. This is a metric of the degree of overlap that does not employ point distances. Its value is obtained without actually using any 3D grid data structure, with a computational complexity of order O(n), where n represents the number of laser points. This objective function is optimized with a globalized version of the downhill Simplex algorithm to avoid local minima. Experimental results are presented from indoor and outdoor environments with different degrees of structuring. The effect of cube size and the number of vertices on registration performance has been analyzed. Besides, experiments show that the proposed method achieves similar accuracy as iterative closest points (ICP) and normal distribution transform (NDT), while it improves both computation time and robustness against initial misalignments.

Boresight Calibration of Construction Misalignments for 3D Scanners Built with a 2D Laser Rangefinder Rotating on Its Optical Center

Many applications, like mobile robotics, can profit from acquiring dense, wide-ranging and accurate 3D laser data. Off-the-shelf 2D scanners are commonly customized with an extra rotation as a low-cost, lightweight and low-power-demanding solution. Moreover, aligning the extra rotation axis with the optical center allows the 3D device to maintain the same minimum range as the 2D scanner and avoids offsets in computing Cartesian coordinates. The paper proposes a practical procedure to estimate construction misalignments based on a single scan taken from an arbitrary position in an unprepared environment that contains planar surfaces of unknown dimensions. Inherited measurement limitations from low-cost 2D devices prevent the estimation of very small translation misalignments, so the calibration problem reduces to obtaining boresight parameters. The distinctive approach with respect to previous plane-based intrinsic calibration techniques is the iterative maximization of both the flatness and the area of visible planes. Calibration results are presented for a case study. The method is currently being applied as the final stage in the production of a commercial 3D rangefinder.

Spherical Laser Point Sampling with Application to 3D Scene Genetic Registration

Scene registration of 3D laser rangefinder scans is increasingly being required in applications, such as mobile robotics, that demand a timely response. For speeding up point matching methods, the large amount of range data should be reduced. This sampling, in turn, can have a significant impact on accuracy. In particular, genetic algorithms provide a robust optimization method that avoids local minima for scan matching, but their computational cost grows with the number of points. This paper proposes a new point sampling strategy that considers the spherical scanning process of most sensors to equalize the measure-direction density. This fast sampling method reduces the number of points without loss of relevant scene information. It is experimentally compared with other systematic approaches for the case of actual scene genetic registration.

Using multicore processors to parallelize 3D point cloud registration with the Coarse Binary Cubes method

This paper pursues speeding up 3D point cloud matching, which is crucial for mobile robotics. In previous work, we devised the Coarse Binary Cubes (CBC) method for fast and accurate registration of 3D scenes based on an integer objective function. Instead of point distance calculations, the method optimizes the number of coincident binary cubes between a pair of range images. In this paper, we propose taking advantage of widespread multicore and multithreaded processors to further speed-up CBC by parallel evaluation of prospective solutions in a globalized Nelder-Mead search. A performance analysis on two types of multicore processors is offered for indoor and outdoor scans from a 3D laser rangefinder. The proposed solution achieves a computational time gain close to the number of physical cores.

Driver Assistance System for Passive Multi-Trailer Vehicles with Haptic Steering Limitations on the Leading Unit

Driving vehicles with one or more passive trailers has difficulties in both forward and backward motion due to inter-unit collisions, jackknife, and lack of visibility. Consequently, advanced driver assistance systems (ADAS) for multi-trailer combinations can be beneficial to accident avoidance as well as to driver comfort. The ADAS proposed in this paper aims to prevent unsafe steering commands by means of a haptic handwheel. Furthermore, when driving in reverse, the steering-wheel and pedals can be used as if the vehicle was driven from the back of the last trailer with visual aid from a rear-view camera. This solution, which can be implemented in drive-by-wire vehicles with hitch angle sensors, profits from two methods previously developed by the authors: safe steering by applying a curvature limitation to the leading unit, and a virtual tractor concept for backward motion that includes the complex case of set-point propagation through on-axle hitches. The paper addresses system requirements and provides implementation details to tele-operate two different off- and on-axle combinations of a tracked mobile robot pulling and pushing two dissimilar trailers.