Engineering topics

Ivanov, Tonislav

Publications and source records attributed to Ivanov, Tonislav.

3 records

NASA NTRS2010

Evaluating the Performance of Unmanned Ground Vehicle Water Detection

Water detection is a critical perception requirement for unmanned ground vehicle (UGV) autonomous navigation over cross-country terrain. During the Robotics Collaborative Technology Alliances (RCTA) program, the Jet Propulsion Laboratory (JPL) developed a set of water detection algorithms that are used to detect, localize, and avoid water bodies large enough to be a hazard to a UGV. The JPL water detection software performs the detection and localization stages using a forward-looking stereo pair of color cameras. The 3D coordinates of water body surface points are then output to a UGV's autonomous mobility system, which is responsible for planning and executing safe paths. There are three primary methods for evaluating the performance of the water detection software. Evaluations can be performed in image space on the intermediate detection product, in map space on the final localized product, or during autonomous navigation to characterize the avoidance of a variety of water bodies. This paper describes a methodology for performing the first two types of water detection performance evaluations.

stereo vision↗

NASA NTRS2010

Analysis of Flash Lidar Field Test Data for Safe Lunar Landing

In May 2008, the Autonomous Landing and Hazard Avoidance Technology (ALHAT) Project conducted a helicopter field test of a commercial flash lidar to assess its applicability to safe lunar landing. The helicopter flew several flights, which covered a variety of slant ranges and viewing angles, over man-made and natural lunar-like terrains. The collected data were analyzed to assess the performance of the sensor and the performance of two algorithms: Hazard Detection (HD) and Hazard Relative Navigation (HRN). The collected flash lidar data were also used to validate a high fidelity flash lidar software model used in ALHAT Monte Carlo simulations. The field test results, combined with prior simulation results, advanced the technology readiness level of the HD algorithm to TRL 5 and the HRN algorithm to TRL 4.

hazard relative navigation↗

NASA NTRS2009

Head Pose Estimation Using Multilinear Subspace Analysis for Robot Human Awareness

Mobile robots, operating in unconstrained indoor and outdoor environments, would benefit in many ways from perception of the human awareness around them. Knowledge of people's head pose and gaze directions would enable the robot to deduce which people are aware of the its presence, and to predict future motions of the people for better path planning. To make such inferences, requires estimating head pose on facial images that are combination of multiple varying factors, such as identity, appearance, head pose, and illumination. By applying multilinear algebra, the algebra of higher-order tensors, we can separate these factors and estimate head pose regardless of subject's identity or image conditions. Furthermore, we can automatically handle uncertainty in the size of the face and its location. We demonstrate a pipeline of on-the-move detection of pedestrians with a robot stereo vision system, segmentation of the head, and head pose estimation in cluttered urban street scenes.

Ivanov, Tonislav↗