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What is Sensor binding?
Sensor fusion refers to the process of combining sensory data from multiple sensors to obtain information that cannot be derived from any individual sensor alone. It provides more complete and accurate understanding of the environment surrounding an autonomous system. By fusing the complementary and redundant information available from sensors of different modalities, the system is able to overcome the limitations and weaknesses of each standalone sensor.
Need for Sensor binding
No single sensor is optimal for obtaining complete environmental information for reliable decision making. Sensors have inherent limitations in their accuracy, resolution, coverage area and susceptibility to noise. Sensor binding aims to address these limitations by leveraging the advantage of each sensor to provide robust and consistent data about the environment.
For example, a camera cannot provide range information on its own while a lidar is unaffected by lighting conditions unlike a camera. By fusing camera and lidar data, the combined system can see objects in low light and know their distances and positions accurately. Similarly, radar and camera complement each other – radar detects objects in adverse weather but does not provide detailed image data, while camera identifies objects precisely but is severely limited in fog and rain. Fusing these heterogeneous sensors enables extraction of richer information compared to using any single sensor alone.
Levels of Sensor binding
Sensor binding techniques can be broadly classified into three levels based on the processing stage at which fusion occurs:
Low Level or Signal Level Fusion: It involves directly working with the raw signals or measurements from individual sensors, before they are interpreted or recognised. At this level, algorithms align, filter and combine the actual sensor outputs to improve accuracy and resolve conflicts or ambiguity. For example, averaging or weighted averaging of corresponding pixels or voxels from camera and lidar outputs.
Mid Level or Feature Level Fusion: At this stage, features are extracted from individual sensor measurements and then combined. Features may include edges, shapes, textures extracted from images or point clouds. Correlation between identified features from different sensors can reveal relationships and improve feature detection. For instance, detecting circles in camera may confirm presence of wheels detected as point clusters in lidar.
High Level or Decision Level Fusion: Higher level information extracted by each sensor is combined to arrive at a confident decision or estimate of the environment. This involves interpreting features to derive parameters and recognizing objects before fusion. Decisions from multiple sensors are fused to reduce uncertainty. An example is classifying an object detected in camera as a vehicle and fusing it with object classification from lidar to confidently label it as a car.
Algorithms for Sensor binding
Various algorithms are commonly employed for sensor data fusion depending on the types of sensors and the level of processing:
Kalman Filtering: One of the most widely used algorithms, it is a recursive filter that estimates the internal state of a process or system from a series of incomplete and noisy measurements over time. It works well for fusing positional data from IMU, GPS etc. to obtain more accurate estimates.
Bayesian Filtering: Based on Bayes’ theorem of conditional probability, it estimates the posterior probability distribution of the state based on prior knowledge and measurements. Particle filtering, a variant used for nonlinear/non-Gaussian problems like object tracking fusion.
Dempster–Shafer Theory: Also called evidence theory, it allows expressing the degree of belief for a proposition based on multiple sources of uncertain information represented as weighted masses. Finds total belief given individual sensor beliefs.
Fuzzy Logic: Inspired by human reasoning that accounts for partial truth rather than absolute true or false. Helps combine imprecise or uncertain information, for example sensory attributes like colour, shape, size etc.
Neural Networks: Powerful machine learning methods for pattern recognition and prediction. Used extensively for high level Sensor binding like object classification by training networks on fused heterogeneous datasets. Examples include CNNs for image-lidar fusion.
Applications of Sensor binding
Some major application domains that have benefited from advancements in Sensor binding technology include:
Autonomous Vehicles: Fusing data from cameras, lidars, radars and other sensors allows self-driving cars to accurately perceive the surrounding environment in all conditions for safe navigation.
Robotics: Robot manipulation, mobility, grasping heavily relies on fused sensory feedback for tasks like object detection, localization, mapping in applications like industrial automation, space robots, rescue robots etc.
Virtual/Augmented Reality: Sensor binding enables robust tracking capabilities in VR/AR by combining data from IMUs, cameras, depth sensors to precisely determine user position, motions and interactions with virtual content.
Drones/UAVs: Miniature aerial vehicles leverage Sensor binding for functions like simultaneous localization and mapping, collision avoidance, terrain following especially in GPS-denied environments.
Defence/Surveillance: Military weaponry and security systems benefit from fusion of electro-optical, infrared cameras with radar and other intelligent sensors to enhance situational awareness.
Medical Equipment: Devices like CT, MRI utilize data fusion methods for improved diagnosis. Also finds uses in prosthetics, rehabilitation robotics through integration of biological and artificial sensory modalities.
Challenges in Sensor binding
While Sensor binding provides clear advantages, there are also challenges that need to be addressed:
Registration Error: Precisely aligning measurements from different sensors in a common coordinate frame with minimal misalignments and delays is technically complex.
Hardware Limitations: Processing power and memory required for resource-intensive fusion algorithms may exceed capabilities of embedded platforms. Real-time constraints also pose practical challenges.
Occlusion/Noise: Presence of occlusions or noise in data from some sensors affect fusion outputs. Noise filtering and outlier rejection play important role.
Inter-dependency: Effectiveness hinges on designing heterogeneous but complementary sensor suites. Sub-optimal or sub-standard components degrade overall results.
Lack of Ground Truth: Absence of reference data for validating and benchmarking fusion accuracy, especially in complex real-world conditions. Simulated data helps but limited.
Privacy Issues: Careful regulation needed as capturing and fusing diverse multi-sensory data poses privacy and security risks if misused.
The Future of Sensor binding
As sensors, computing power and algorithms continue to improve at a rapid pace, Sensor binding will play an increasingly important role across many application domains. Advances on the horizon include:
– Context-aware and adaptive fusion based on environmental conditions
– Deep learning based end-to-end fusion models
– Multi-Sensor binding at the chip-level for IoT/edge devices
– Novel sensors for new modalities will enable richer information fusion
– Automated calibration and registration techniques
– Privacy-aware distributed fusion architectures
– Explainable Sensor binding using simulation and reasoning
In conclusion, sensor fusion leverages the strengths of multiple sensors to provide more accurate and complete situational awareness than possible with individual sensors alone. It holds tremendous potential to enhance capabilities, especially for autonomous systems and applications requiring robust perception in unstructured real-world conditions. Advancementsin multisensory data integration will continue shaping new technologies and applications in the future.
*Note:
- Source: Coherent Market Insights, Public sources, Desk research
- We have leveraged AI tools to mine information and compile it
