JD
J.F.M. Domhof
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We address joint extrinsic calibration of lidar, camera and radar sensors. To simplify calibration, we propose a single calibration target design for all three modalities, and implement our approach in an open-source tool with bindings to Robot Operating System (ROS). Our tool features three optimization configurations, namely using error terms for a minimal number of sensor pairs, or using terms for all sensor pairs in combination with loop closure constraints, or by adding terms for structure estimation in a probabilistic model. Apart from relative calibration where relative transformations between sensors are computed, our work also addresses absolute calibration that includes calibration with respect to the mobile robot's body. Two methods are compared to estimate the body reference frame using an external laser scanner, one based on markers and the other based on manual annotation of the laser scan. In the experiments, we evaluate the three configurations for relative calibration. Our results show that using terms for all sensor pairs is most robust, especially for lidar to radar, when minimum five board locations are used. For absolute calibration the median rotation error around the vertical axis reduces from 1. before calibration, to 0.33. using the markers and 0.02. with manual annotations.
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We address joint extrinsic calibration of lidar, camera and radar sensors. To simplify calibration, we propose a single calibration target design for all three modalities, and implement our approach in an open-source tool with bindings to Robot Operating System (ROS). Our tool features three optimization configurations, namely using error terms for a minimal number of sensor pairs, or using terms for all sensor pairs in combination with loop closure constraints, or by adding terms for structure estimation in a probabilistic model. Apart from relative calibration where relative transformations between sensors are computed, our work also addresses absolute calibration that includes calibration with respect to the mobile robot's body. Two methods are compared to estimate the body reference frame using an external laser scanner, one based on markers and the other based on manual annotation of the laser scan. In the experiments, we evaluate the three configurations for relative calibration. Our results show that using terms for all sensor pairs is most robust, especially for lidar to radar, when minimum five board locations are used. For absolute calibration the median rotation error around the vertical axis reduces from 1. before calibration, to 0.33. using the markers and 0.02. with manual annotations.
We present a novel open-source tool for extrinsic calibration of radar, camera and lidar. Unlike currently available offerings, our tool facilitates joint extrinsic calibration of all three sensing modalities on multiple measurements. Furthermore, our calibration target design extends existing work to obtain simultaneous measurements for all these modalities. We study how various factors of the calibration procedure affect the outcome on real multi-modal measurements of the target. Three different configurations of the optimization criterion are considered, namely using error terms for a minimal amount of sensor pairs, or using terms for all sensor pairs with additional loop closure constraints, or by adding terms for structure estimation in a probabilistic model. The experiments further evaluate how the number of calibration boards affect calibration performance, and robustness against different levels of zero mean Gaussian noise. Our results show that all configurations achieve good results for lidar to camera errors and that fully connected pose estimation shows the best performance for lidar to radar errors when more than five board locations are used.
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We present a novel open-source tool for extrinsic calibration of radar, camera and lidar. Unlike currently available offerings, our tool facilitates joint extrinsic calibration of all three sensing modalities on multiple measurements. Furthermore, our calibration target design extends existing work to obtain simultaneous measurements for all these modalities. We study how various factors of the calibration procedure affect the outcome on real multi-modal measurements of the target. Three different configurations of the optimization criterion are considered, namely using error terms for a minimal amount of sensor pairs, or using terms for all sensor pairs with additional loop closure constraints, or by adding terms for structure estimation in a probabilistic model. The experiments further evaluate how the number of calibration boards affect calibration performance, and robustness against different levels of zero mean Gaussian noise. Our results show that all configurations achieve good results for lidar to camera errors and that fully connected pose estimation shows the best performance for lidar to radar errors when more than five board locations are used.
This paper presents a systematic approach to evaluate the tracking performance limits for different sensor modalities (lidar, radar and vision) and for combination of these sensors modalities. The Cramer-Rao lower bound (CRLB) is used to predict the tracking performance limits for state of the art sensors such as the Continental ARS408 radar, Velodyne HDL-64E lidar and a state of the art monocular/stereo camera. The performance is evaluated by computing the theoretical CRLB in urban and highway environments. In both scenarios, the best performance was achieved by a combination of lidar and radar. In the close range, stereo vision improves the longitudinal tracking performance limits. Furthermore, radar is crucial on highways because of the quick longitudinal convergence characteristics.
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This paper presents a systematic approach to evaluate the tracking performance limits for different sensor modalities (lidar, radar and vision) and for combination of these sensors modalities. The Cramer-Rao lower bound (CRLB) is used to predict the tracking performance limits for state of the art sensors such as the Continental ARS408 radar, Velodyne HDL-64E lidar and a state of the art monocular/stereo camera. The performance is evaluated by computing the theoretical CRLB in urban and highway environments. In both scenarios, the best performance was achieved by a combination of lidar and radar. In the close range, stereo vision improves the longitudinal tracking performance limits. Furthermore, radar is crucial on highways because of the quick longitudinal convergence characteristics.
This paper proposes a quality of service multi-sensor bootstrap filter for automated driving that deals with time-varying or state dependent conditions. In this way, the reliability of the sensor data fusion system is continuously evaluated in order to detect potentially dangerous conditions such as sensor failure or adverse environmental conditions such as rain and fog. Simulations show that the proposed robust multi-sensor bootstrap filter is able to robustly estimate the quality of service of the sensors. Furthermore, the filter outperforms tracking filters that assume a perfect detection profile. In addition, real world experiments in a fog simulator show that the proposed multi-sensor local-bootstrap-LMB filter outperforms all other filters in foggy conditions.
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This paper proposes a quality of service multi-sensor bootstrap filter for automated driving that deals with time-varying or state dependent conditions. In this way, the reliability of the sensor data fusion system is continuously evaluated in order to detect potentially dangerous conditions such as sensor failure or adverse environmental conditions such as rain and fog. Simulations show that the proposed robust multi-sensor bootstrap filter is able to robustly estimate the quality of service of the sensors. Furthermore, the filter outperforms tracking filters that assume a perfect detection profile. In addition, real world experiments in a fog simulator show that the proposed multi-sensor local-bootstrap-LMB filter outperforms all other filters in foggy conditions.