Wheel odometry-based car localization and tracking on vectorial map

International audienceIn this paper, we present a car self-localization approach based on free inputs. We propose to use wheel speeds, which is available on most car through the CAN bus, and community developed road maps. A particle filter framework is used to achieve self-localization on a graph-based representation of a road map. Our results suggests that self-localization and tracking are feasible with these two inputs at a really low computational cost. Car self-localization is achieved with an averaged 5 m accuracy within a 100 km drivable road map on a 12 km sequence

Fast and robust vehicle positioning on graph-based representation of drivable maps

International audienc

Numerical Investigation of an Axis-based Approach to Rigid Registration

The term rigid registration identifies the process that optimally aligns different data sets whose information has to be merged, as in the case of robot calibration, image-guided surgery or patient-specific gait analysis. One of the most common approaches to rigid registration relies on the identifica-tion of a set of fiducial points in each data set to be registered to compute the rototranslational matrix that optimally aligns them. Both measurement and hu-man errors directly affect the final accuracy of the process. Increasing the number of fiducials may improve registration accuracy but it will also increase the time and complexity of the whole procedure, since correspondence must be estab-lished between fiducials in different data sets. The aim of this paper is to present a new approach that resorts to axes instead of points as fiducial features. The fundamental advantage is that any axis can be easily identified in each data set by least-square linear fitting of multiple, un-sorted measured data. This provides a way to filtering the measurement error within each data set, improving the registration accuracy with a reduced effort. In this work, a closed-form solution for the optimal axis-based rigid registration is presented. The accuracy of the method is compared with standard point-based rigid registration through a numerical test. Axis-based registration results one or-der of magnitude more accurate than point-based registration

Evaluating an Accelerometer-Based System for Spine Shape Monitoring

In western societies a huge percentage of the population suffers from some kind of back pain at least once in their life. There are several approaches addressing back pain by postural modifications. Postural training and activity can be tracked by various wearable devices most of which are based on accelerometers. We present research on the accuracy of accelerometer-based posture measurements. To this end, we took simultaneous recordings using an optical motion capture system and a system consisting of five accelerometers in three different settings: On a test robot, in a template, and on actual human backs. We compare the accelerometer-based spine curve reconstruction against the motion capture data. Results show that tilt values from the accelerometers are captured highly accurate, and the spine curve reconstruction works well