Stefan Krause

Remission based improvement of extrinsic parameter calibration of camera and laser scanner

This paper presents an improvement for an automatic extrinsic parameter calculation between a monocular camera and single line laser range finder. The focus of this work is a further reduction of calibration errors compared to other existing methods, and the unique and automatic identification and detection of a calibration object under usage of the remission measurements of the laser scanner data. The use of the remission measurements leads to a reduction of the edge effect, which leads to error measurements at the edges of a measured object, if distance measurements are used exclusively. Further, the usage of the remission measurements enables the unique identification of a new calibration object with a significant remission signature. The result is an automatic extrinsic parameter calculation which works reliably with a high accuracy. The performance of the proposed method was verified in simulation and experiments. In order to classify the results of simulation and experiments, they were compared to other existing methods.

Multi-Purpose Environment Awareness Approach for Single Line Laser Scanner in a Small Rotorcraft UA

The work presents an environment awareness approach for a small rotorcraft unmanned aircraft (UA) which operates at low height using a single line laser scanner which enables a height estimation with a concurrent detection of ground fixed obstacles. The approach is suitable for small UA which are not able to carry complex and heavy 3D laser scanner mountings having additional drives or mirrors. It works without using external reference systems like DGPS. The approach was especially developed for a mission of the “International Micro Air Vehicle Conference” outdoor contest, where it is the aim to fly through a 6x6m artificial gate. The sensor data processing enables the height estimation above ground as well as the detection of obstacles in order to meet the mission’s goal. The height estimation enables a near-ground flight to prevent a collision with a top boundary of the gate, and a terrain following. The obstacle detection senses the pillars of the gate and finds a safe way through the narrow gate passage. The development and optimisation of the mounting and the sensor processing, as well as the validation, was realized under operational conditions with manual remote control (RC) helicopter flights and virtual flights at a simulation environment. The results of the experiments show that with this approach the mission can be fulfilled as a reliable ground estimation and object detection is ensured.

Optical-Aided Aircraft Navigation using Decoupled Visual SLAM with Range Sensor Augmentation

This paper presents an optical-aided navigation method for automatic flights where satellite navigation might be disturbed. The proposed solution follows common approaches where satellite position updates are replaced with measurements from environment sensors such as a camera, lidar or radar as required. The alternative positioning is determined by a localization and mapping (SLAM) algorithm that handles 2D feature inputs from monocular camera images as well as 3D inputs from camera images that are augmented by range measurements. The method requires neither known landmarks nor a globally flat terrain. Beside the visual SLAM algorithm, the paper describes how to generate 3D feature inputs from lidar and radar sources and how to benefit from both monocular triangulation and 3D features. Regarding state estimation, the approach decouples visual SLAM from the filter updates. This allows software and hardware separation, i.e. visual SLAM computations on powerful hardware while the main filter can be installed on real-time hardware with possible lower capabilities. The localization quality in case of satellite dropouts is tested with data sets from manned and unmanned flights with different sensors while keeping all parameters constant. The tests show the applicability of this method in flat and hilly terrain and with different path lengths from few hundred meters to many kilometers. The relative navigation achieves an accumulation error of 1–6 % of distance traveled depending on the flight scenario. In addition to the flights, the paper discusses flight profile limitations when optical navigation methods are used.

Optical aircraft navigation with multi-sensor SLAM and infinite depth features

In the context of optical-aided navigation and visual Simultaneous Localization And Mapping (SLAM) for satellite-denied aircraft navigation, this paper extends the monocular SLAM approach by the use of multiple sensors with different viewing directions. Downward optical sensors see other movements than forward-looking cameras, hence it is straightforward to combine the benefits of both. This combination helps to estimate all the six motion components with increased robustness, yielding a more stable and accurate state estimation for optical-aided navigation solutions. The method is evaluated with aerial data from manned and unmanned flights. In the data analysis, a satellite navigation dropout is simulated, and the flight trajectory is then reconstructed just by the optical data. The method is tested in small-scale scenarios as well as in longer flights with several kilometers of flight range. The results show some increased performance of an additional forward camera in comparison to a setup with only downward sensors. It is proposed to use such multi-sensor configurations wherever motion estimation ambiguities with a single camera are probable, especially when larger distances have to be flown with optical navigation.

Prevention 0f Unwanted Free-Declaration of Static Obstacles in Probability Occupancy Grids

Obstacle detection and avoidance are major research fields in unmanned aviation. Map based obstacle detection approaches often use discrete world representations such as probabilistic grid maps to fuse incremental environment data from different views or sensors to build a comprehensive representation. The integration of continuous measurements into a discrete representation can result in rounding errors which, in turn, leads to differences between the artificial model and real environment. The cause of these deviations is a low spatial resolution of the world representation comparison to the used sensor data. Differences between artificial representations which are used for path planning or obstacle avoidance and the real world can lead to unexpected behavior up to collisions with unmapped obstacles. This paper presents three approaches to the treatment of errors that can occur during the integration of continuous laser measurement in the discrete probabilistic grid. Further, the quality of the error prevention and the processing performance are compared with real sensor data.

Visual detection of air-to-air refueling drogue

We present an approach for the automatic visual detection and position estimation of an air-to-air refueling drogue for unmanned aircraft. The drogue, which is dragged by an ahead flying tanker, is recorded by a passive color camera. Based on the flashy color of the drogue, the image is segmented in probable drogues and background. The drogues prediction is specified by a circle shape identification, to determine the drogue position in 3D camera frame also if parts of the drogue are covered. Finally, the drogue is tracked and classified over several images, to sort out one-time identifications. The presented approach is validated with a simulation setup that bases on real flight test experiences. The validation shows that the presented method generally enables a detection of the air-to-air refueling drogue. The results are discussed with respect to the effect of covering the drogue by tanker or image frame and the influence of the blooming effect during the distance estimation.