Network Uncertainty Informed Semantic Feature Selection for Visual SLAM

In order to facilitate long-term localization using a visual simultaneous localization and mapping (SLAM) algorithm, careful feature selection can help ensure that reference points persist over long durations and the runtime and storage complexity of the algorithm remain consistent. We present SIVO (Semantically Informed Visual Odometry and Mapping), a novel information-theoretic feature selection method for visual SLAM which incorporates semantic segmentation and neural network uncertainty into the feature selection pipeline. Our algorithm selects points which provide the highest reduction in Shannon entropy between the entropy of the current state and the joint entropy of the state, given the addition of the new feature with the classification entropy of the feature from a Bayesian neural network. Each selected feature significantly reduces the uncertainty of the vehicle state and has been detected to be a static object (building, traffic sign, etc.) repeatedly with a high confidence. This selection strategy generates a sparse map which can facilitate long-term localization. The KITTI odometry dataset is used to evaluate our method, and we also compare our results against ORB_SLAM2. Overall, SIVO performs comparably to the baseline method while reducing the map size by almost 70%.Comment: Published in: 2019 16th Conference on Computer and Robot Vision (CRV

SIVO: Semantically Informed Visual Odometry and Mapping

Accurate localization is a requirement for any autonomous mobile robot. In recent years, cameras have proven to be a reliable, cheap, and effective sensor to achieve this goal. Visual simultaneous localization and mapping (SLAM) algorithms determine camera motion by tracking the motion of reference points from the scene. However, these references must be static, as well as viewpoint, scale, and rotation invariant in order to ensure accurate localization. This is especially paramount for long-term robot operation, where we require our references to be stable over long durations and also require careful point selection to maintain the runtime and storage complexity of the algorithm while the robot navigates through its environment. In this thesis, we present SIVO (Semantically Informed Visual Odometry and Mapping), a novel feature selection method for visual SLAM which incorporates machine learning and neural network uncertainty into an information-theoretic approach to feature selection. The emergence of deep learning techniques has resulted in remarkable advances in scene understanding, and our method supplements traditional visual SLAM with this contextual knowledge. Our algorithm selects points which provide significant information to reduce the uncertainty of the state estimate while ensuring that the feature is detected to be a static object repeatedly, with a high confidence. This is done by evaluating the reduction in Shannon entropy between the current state entropy, and the joint entropy of the state given the addition of the new feature with the classification entropy of the feature from a Bayesian neural network. Our method is evaluated against ORB SLAM2 and the ground truth of the KITTI odometry dataset. Overall, SIVO performs comparably to ORB SLAM2 (average of 0.17% translation error difference, 6.2 × 10 −5 deg/m rotation error difference) while removing 69% of the map points on average. As the reference points selected are from static objects (building, traffic signs, etc.), the map generated using our algorithm is suitable for long-term localization

Encoderless Gimbal Calibration of Dynamic Multi-Camera Clusters

Dynamic Camera Clusters (DCCs) are multi-camera systems where one or more cameras are mounted on actuated mechanisms such as a gimbal. Existing methods for DCC calibration rely on joint angle measurements to resolve the time-varying transformation between the dynamic and static camera. This information is usually provided by motor encoders, however, joint angle measurements are not always readily available on off-the-shelf mechanisms. In this paper, we present an encoderless approach for DCC calibration which simultaneously estimates the kinematic parameters of the transformation chain as well as the unknown joint angles. We also demonstrate the integration of an encoderless gimbal mechanism with a state-of-the art VIO algorithm, and show the extensions required in order to perform simultaneous online estimation of the joint angles and vehicle localization state. The proposed calibration approach is validated both in simulation and on a physical DCC composed of a 2-DOF gimbal mounted on a UAV. Finally, we show the experimental results of the calibrated mechanism integrated into the OKVIS VIO package, and demonstrate successful online joint angle estimation while maintaining localization accuracy that is comparable to a standard static multi-camera configuration.Comment: ICRA 201