Automatic Calibration of Multiple Coplanar Sensors

This paper describes an algorithm for recovering the rigid 3-DOF transformation (offset and rotation) between pairs of sensors mounted rigidly in a common plane on a mobile robot. The algorithm requires only a set of sensor observations made as the robot moves along a suitable path. Our method does not require synchronized sensors; nor does it require complete metrical reconstruction of the environment or the sensor path. We show that incremental pose measurements alone are sufficient to recover sensor calibration through nonlinear least squares estimation. We use the Fisher Information Matrix to compute a Cramer-Rao lower bound (CRLB) for the resulting calibration. Applying the algorithm in practice requires a non-degenerate motion path, a principled procedure for estimating per-sensopose displacements and their covariances, a way to temporally resample asynchronous sensor data, and a way to assess the quality of the recovered calibration. We give constructive methods for each step. We demonstrate and validate the end-to-end calibration procedure for both simulated and real LIDAR and inertial data, achieving CRLBs, and corresponding calibrations, accurate to millimeters and milliradians. Source code is available from http://rvsn.csail.mit.edu/calibration

Sensor fusion for flexible human-portable building-scale mapping

This paper describes a system enabling rapid multi-floor indoor map building using a body-worn sensor system fusing information from RGB-D cameras, LIDAR, inertial, and barometric sensors. Our work is motivated by rapid response missions by emergency personnel, in which the capability for one or more people to rapidly map a complex indoor environment is essential for public safety. Human-portable mapping raises a number of challenges not encountered in typical robotic mapping applications including complex 6-DOF motion and the traversal of challenging trajectories including stairs or elevators. Our system achieves robust performance in these situations by exploiting state-of-the-art techniques for robust pose graph optimization and loop closure detection. It achieves real-time performance in indoor environments of moderate scale. Experimental results are demonstrated for human-portable mapping of several floors of a university building, demonstrating the system's ability to handle motion up and down stairs and to organize initially disconnected sets of submaps in a complex environment.Lincoln LaboratoryUnited States. Air Force (Contract FA8721-05-C-0002)United States. Office of Naval Research (Grant N00014-10-1-0936)United States. Office of Naval Research (Grant N00014-11-1-0688)United States. Office of Naval Research (Grant N00014-12-10020

Articulated pose estimation via over-parametrization and noise projection

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.Cataloged from PDF version of thesis.Includes bibliographical references (pages 155-161).Outside the factory, robots will often encounter mechanical systems with which they need to interact. The robot may need to open and unload a kitchen dishwasher or move around heavy construction equipment. Many of the mechanical systems encountered can be described as a series of rigid segments connected by joints. The pose of a segment places constraints on adjacent segments because they are mechanically 'connected. When modeling or perceiving the motion of such an articulated system, it is beneficial to make use of these constraints to reduce uncertainty. In this thesis, we examine two aspects of perception related to articulated structures. First, we examine the special case of a single segment and recover the rigid body transformation between two sensors mounted on it. Second, we consider the task of tracking the configuration of a multi-segment structure, given some knowledge of its kinematics. First, we develop an algorithm to recover the rigid body transformation, or extrinsic calibration, between two sensors on a link of a mobile robot. The single link, a degenerate articulated object, is often encountered in practice. The algorithm requires only a set of sensor observations made as the robot moves along a suitable path. Over-parametrization of poses avoids degeneracies and the corresponding Lie algebra enables noise projection to and from the over-parametrized space. We demonstrate and validate the end-to-end calibration procedure, achieving Cramer-Rao Lower Bounds. The parameters are accurate to millimeters and milliradians in the case of planar LIDARs data and about 1 cm and 1 degree for 6-DOF RGB-D cameras. Second, we develop a particle filter to track an articulated object. Unlike most previous work, the algorithm accepts a kinematic description as input and is not specific to a particular object. A potentially incomplete series of observations of the object's links are used to form an on-line estimate of the object's configuration (i.e., the pose of one link and the joint positions). The particle filter does not require a reliable state transition model, since observations are incorporated during particle proposal. Noise is modeled in the observation space, an over-parametrization of the state space, reducing the dependency on the kinematic description. We compare our method to several alternative implementations and demonstrate lower tracking error for fixed observation noise.by Jonathan David Brookshire.Ph. D