Using Neuromorphic Camera, an image is captured that reflects the dynamic range of the subject with better temporal resolution than the typical frame-based, without motion blur neural networks. This system overcomes high tracking errors and depth uncertainties.
In-vehicle visual odometry system
Identifying the position of a mobile robot using vision based odometry is an important task for the development of autonomous driving operations. There are several ways to approach this problem. One method is feature-based approach, which is suitable for textured environments, and another is appearance-based approach, which is suitable for untextured environments.
One of the main goals in the research on robotics is accurate localization of vehicles. This task is fundamental. It requires accurate self-localization techniques, which will allow expansion of autonomous driving operations. In order to perform accurate localization, the following methods are evaluated:utilising a camera, LiDAR, and many modalities. The results of the evaluation are then subjected perform a series of tests on driving datasets from public roads.
The rate of local drift under vision-based odometry is lower than that of low-precision INS, wheel encoders, and other expensive localization sensors. This makes VO suitable for autonomous driving in situations where GPS signals are unavailable. The VO system provides incremental online estimation of the vehicle’s position, based on a stream of images captured by the vehicle camera.
The VO system is an effective non-contact positioning technique for mobile robots. It is also a good replacement for expensive sensors. VO is characterized by good balance of reliability, implementation complexity, and cost. It can be combined with an INS or GPS to provide accurate localization. VO is an inexpensive alternative to conventional techniques.
Visual Inertial Odometry uses inertial measurements from the vehicle IMU to compute the vehicle’s relative motion, based on image information from the camera. It is commonly used to navigate vehicles in situations where GPS signals are unavailable. The relative position error of the VO system ranges from 0.1 to 2%.
The VO system is not affected by wheel slippage or uneven terrain. It is also more accurate than the conventional techniques. It can work in environments with low light levels. The accuracy of VO estimation is improved by the use of a Bayesian filter called the Kalman filter. The Kalman filter compares prior vehicle state estimates with the current observations. The Kalman filter minimizes the mean squared error of the estimates.
EKLT-VIO overcomes high tracking errors and depth uncertainties in Neuromorphic Camera
Among the countless VIO solutions on the market, one stands out for its sheer novelty. EKLT-VIO is a state-based frontend coupled with a backend that features the aforementioned state-based technology, but it takes the cake by virtue of its own performance and the resulting fidelity. The aforementioned state-based technology is coupled with an optimized filter to handle the lion’s share of the feature data, but that’s a story for another day. The EKLT-VIO solution is also able to outperform its predecessors on a per frame basis. The aforementioned algorithm also enables persistent tracking of powerlines for the next 10x longer than the average powerline tracking algorithm.
The aforementioned solution is not without its flaws, most notably, the inability to track objects in close proximity to each other without having to rely on a centralized teleport. It also has to deal with motion blur and low light conditions. The problem is solved by leveraging the complementarity of the event camera and a standard camera, and is also aided by a specialized loss training algorithm. The aforementioned solution also makes use of the other aforementioned gyroscope and accelerometer biases to improve the pose estimation of the former, making it a winning triumvirate. The aforementioned solution also boasts low latency, making it a desirable proposition even for the most jaded of the skeptics. Lastly, the aforementioned solution also boasts a lower cost of ownership. In short, EKLT-VIO is a harbinger of the future, and may be the answer to your next boarding pass, albeit in a non-intrusive manner. The aforementioned solution is not without its limitations, but it can make a formidable competitor to the likes of VIO solutions in the crowded field. Ultimately, the aforementioned solution can make your agent a better navigator while saving you from the dreaded debugging rites. With a small investment in time and money, your agent can be on his way to greater heights in no time. For more information, contact us. We’ll be happy to help! Our experts are at your disposal via email, phone, or live chat. You can also take advantage of our expertise by requesting a free demo or trial.
Spike camera and its coding methods
Using a neuromorphic camera to capture high speed dynamic scenes is an attractive prospect. Its advantages include the ability to generate continuous spikes and the capacity to record a high dynamic range (HDR) of 120 dB. It also allows for the recording of long-term continuous streams with labels. This technology can be utilized for competitive object detection in challenging scenes. It can be implemented in neuromorphic processors using weight-dependent STDP algorithms.
Spike encoding is an important step in the pipeline of neuromorphic computing. It represents light intensity as spikes and ensures important information is preserved for classification. Different coding methods are proposed to represent this information.
The authors used a biological two-layer SNN trained with the STDP algorithm to study the various coding schemes. They analyzed the various features using pruning and quantization techniques. They then evaluated the performance of the different coding schemes using different metrics.
The authors found that rate encoding was robust to noise in input spikes. This is because it requires counting spikes over long time windows to represent information. The rate is fed into a machine learning classifier. The authors then compared the performance of various coding schemes using the MNIST dataset.
They found that the SNN’s learning performance depended on the spike encoding scheme. They found that rate coding was more robust.
The performance of an event-based sensor was also shown to be more reliable than a frame-based sensor, according to the authors. Because a frame-based camera has a frame rate bottleneck. The performance of the event-based sensor was measured by considering network performance, synaptic operations, synaptic noise resilience and classification accuracy.
Aside from the spike encoding & evaluation baseline, there are several other reference systems that are available to evaluate the performance of various neural architectures. Some of these reference systems include the spiking reservoir and the event-based detection data set. These reference systems are intended to provide a comparison for various types of neural architectures and to validate the performance of various coding methods.
DVS records a greater dynamic range
Unlike other cameras, neuromorphic camera capture per-pixel brightness changes, asynchronously. This allows for a higher temporal resolution and a wider dynamic range than traditional cameras. The data from these sensors can be processed by spiking neural networks. This technique exploits the time-series nature of event data, and is useful for real-time tracking. It also makes use of the asynchronous nature of the sensor to improve accuracy.
To process the asynchronous output of these sensors, traditional vision algorithms cannot be used. Instead, a new model needs to take advantage of the high temporal resolution of the DVS. This is a challenge in the field of computer vision. It requires an algorithm that is able to process both synchronous and asynchronous events simultaneously. It also needs to provide a fast and effective solution.
In addition to being able to process output from event-based sensors, spiking neural networks can also process asynchronous sensor data. This is made possible by specialized neuromorphic hardware. The spiking neural network can process asynchronous sensor data without the need for pre-processing.
This model also has the ability to reconstruct dynamic vision sensor event data from a compressed bitstream. It was tested on two neuromorphic cameras and a five hour traffic recording. It showed similar performance to existing event-based feature trackers. It also eliminated noise and preserved meaningful scene events. This is the first solution that successfully applied to different vision tasks with event cameras.
Unlike conventional cameras, the DVS is able to capture a wide dynamic range, even under fast motion. It has low power consumption and a small latency. This allows it to be used in safety-critical applications. Its asynchronous nature also eliminates the need for motion blur.
In addition, event cameras are able to provide auxiliary visual information in blind-time between frames. This information can be used to estimate relative displacement. This is also useful in object recognition.
