Digital video source identification based on green-channel photo response non-uniformity (G-PRNU)

This paper proposes a simple but yet an effective new method for the problem of digital video camera identification. It is known that after an exposure time of 0.15 seconds, the green channel is the noisiest of the three RGB colour channels [5]. Based on this observation, the digital camera pattern noise reference, which is extracted using only the green channel of the frames and is called Green-channel Photo Response Non-Uniformity (G-PRNU), is exploited as a fingerprint of the camera. The green channels are first resized to a standard frame size (512x512) using bilinear interpolation. Then the camera fingerprint is obtained by a wavelet based denoising filter described in [4] and averaged over the frames. 2-D correlation coefficient is used in the detection test. This method has been evaluated using 290 video sequences taken by four consumer digital video cameras and two mobile phones. The results show G- PRNU has potential to be a reliable technique in digital video camera identification, and gives better results than PRNU

Continuous-Time Fixed-Lag Smoothing for LiDAR-Inertial-Camera SLAM

Localization and mapping with heterogeneous multi-sensor fusion have been prevalent in recent years. To adequately fuse multi-modal sensor measurements received at different time instants and different frequencies, we estimate the continuous-time trajectory by fixed-lag smoothing within a factor-graph optimization framework. With the continuous-time formulation, we can query poses at any time instants corresponding to the sensor measurements. To bound the computation complexity of the continuous-time fixed-lag smoother, we maintain temporal and keyframe sliding windows with constant size, and probabilistically marginalize out control points of the trajectory and other states, which allows preserving prior information for future sliding-window optimization. Based on continuous-time fixed-lag smoothing, we design tightly-coupled multi-modal SLAM algorithms with a variety of sensor combinations, like the LiDAR-inertial and LiDAR-inertial-camera SLAM systems, in which online timeoffset calibration is also naturally supported. More importantly, benefiting from the marginalization and our derived analytical Jacobians for optimization, the proposed continuous-time SLAM systems can achieve real-time performance regardless of the high complexity of continuous-time formulation. The proposed multi-modal SLAM systems have been widely evaluated on three public datasets and self-collect datasets. The results demonstrate that the proposed continuous-time SLAM systems can achieve high-accuracy pose estimations and outperform existing state-of-the-art methods. To benefit the research community, we will open source our code at ~\url{https://github.com/APRIL-ZJU/clic}

GPS-denied multi-agent localization and terrain classification for autonomous parafoil systems

Guided airdrop parafoil systems depend on GPS for localization and landing. In some scenarios, GPS may be unreliable (jammed, spoofed, or disabled), or unavailable (indoor, or extraterrestrial environments). In the context of guided parafoils, landing locations for each system must be pre-programmed manually with global coordinates, which may be inaccurate or outdated, and offer no in-flight adaptability. Parafoil systems in particular have constrained motion, communication, and on-board computation and storage capabilities, and must operate in harsh conditions. These constraints necessitate a comprehensive approach to address the fundamental limitations of these systems when GPS cannot be used reliably. A novel and minimalist approach to visual navigation and multi-agent communication using semantic machine learning classification and geometric constraints is introduced. This approach enables localization and landing site identification for multiple communicating parafoil systems deployed in GPS-denied environments

GPS-denied multi-agent localization and terrain classification for autonomous parafoil systems

Guided airdrop parafoil systems depend on GPS for localization and landing. In some scenarios, GPS may be unreliable (jammed, spoofed, or disabled), or unavailable (indoor, or extraterrestrial environments). In the context of guided parafoils, landing locations for each system must be pre-programmed manually with global coordinates, which may be inaccurate or outdated, and offer no in-flight adaptability. Parafoil systems in particular have constrained motion, communication, and on-board computation and storage capabilities, and must operate in harsh conditions. These constraints necessitate a comprehensive approach to address the fundamental limitations of these systems when GPS cannot be used reliably. A novel and minimalist approach to visual navigation and multi-agent communication using semantic machine learning classification and geometric constraints is introduced. This approach enables localization and landing site identification for multiple communicating parafoil systems deployed in GPS-denied environments

Multi-Model Kalman Filtering for Adaptive Nonuniformity: Correction in Infrared Sensors

This paper presents an adaptive technique for the estimation of nonuniformity parameters of infrared focal-plane arrays that is robust with respect to changes and uncertainties in scene and sensor characteristics. The proposed algorithm is based on using a bank of Kalman filters in parallel. Each filter independently estimates state variables comprising the gain and the bias matrices of the sensor, according to its own dynamical-model parameters, which underly the statistics of the scene and the nonuniformity as well as the temporal drift in the nonuniformity. The supervising component of the algorithm then generates the final estimates of the state variables by forming a weighted superposition of all the estimates rendered by each Kalman filter. The weights are obtained according to the a posteriori -likelihood principle, applied to the family of models by considering the output residual errors associated with each filter. These weights are updated iteratively between blocks of data, providing the estimator the means to follow the dynamics of the scenes and the sensor. The performance of the proposed estimator and its ability to compensate for fixed-pattern noise are tested using both real and simulated data. The real data is obtained using two cameras operating in the mid- and long-wave infrared regime