Northwestern University engineers have unveiled an extremely energy-efficient 3D camera, called SpiderCam, inspired by how jumping spiders judge depth before making a calculated leap.

The research team behind the novel device said their technology could help engineers build wearable technologies, assistive devices, robots, drones, and other futuristic technologies that benefit from quickly and efficiently gauging their surroundings.

How Jumping Spiders Inspired Energy-Efficient SpiderCam

According to a statement announcing SpiderCam, 3D cameras typically estimate image depth in two ways. The first approach involves captured images from multiple viewpoints and comparing them, and the second involves projecting a light source, often an infrared (IR) laser, and then measuring the reflection.

Although both methods can produce realistic three-dimensional images, they can require significant computing power, additional operational energy, and often expensive hardware such as lasers.

Fortunately, millions of years of evolution have resulted in a creature that possesses a hyper-efficient way of estimating depth: the jumping spider.

“Jumping spiders jump to catch prey, to avoid predators and to get around, and that requires excellent vision,” said Northwestern’s Emma Alexander, the study’s corresponding author and an expert in bio-inspired computer vision. However, the researcher notes that their brains are extremely small, requiring the ability to calculate distances in an energy- and computationally efficient way.

According to Alexander, jumping spider eyes have multiple layers of retinas in each eye, rather than just one as in human eyes. When measuring distance, each retina focuses on an image at a slightly different distance, resulting in some images being blurrier than others.

“They see multiple levels of focus at all times,” Alexander explained. “So, they are always collecting pairs of images.”

After collecting these images in real time, the fly’s poppyseed-sized brain compares their sharpness, yielding a surprisingly accurate depth estimate, as evidenced by the creature’s ability to jump with precision.

Prototype Captures Images at Two Distances and Compares Their Blurriness

Inspired by nature’s hyper-efficient, extremely accurate depth estimation method, Alexander’s team decided to build a 3D camera using the same basic approach.

“We wanted to understand whether we could borrow some of the same principles to create an extremely energy-efficient depth sensor that could be used in resource-constrained situations where users don’t have unlimited access to power,” Alexander explained.

Instead of capturing images at multiple distances, as the jumping spider’s eye does, the Northwestern team’s initial design captures two images of the same scene at different focal lengths. Next, the system uses a custom algorithm to analyze the sharpness of the edges and textures captured in each image. The team said this analysis acts as a “translator between blur and distance” in the fly’s brain, resulting in “depth measurements in real time.”

To best approximate the naturally evolved energy efficiency of a jumping spider’s eye, Alexander’s team built the algorithm directly into a customizable computer chip specifically optimized for energy efficiency called a field-programmable gate array (FPGA).

The final prototype integrating the team’s dual-lens system generates depth maps at 32.5 frames per second. Critically, it generates this level of accurate 3D maps while consuming 624 milliwatts of power. For comparison, the team notes that 624 milliwatts is “less energy than used by a standard nightlight.”

Future Versions Could be Even More Energy Efficient

Although the team said that the current version of the SpiderCam is the “first passive FPGA-based 3D camera system to operate below one watt,” they hope to improve its optics and expand its field of view. Once they have a commercial-grade version, the team hopes to integrate SpiderCam into wearable devices and small robots.

Beyond proposed applications, the Northwestern team said that they “envision” a future, ultra-energy-efficient version  of the chip capable of “bringing 3D vision to applications where conventional depth sensors are impractical.”

“I’m very interested in settings where you’re very resource constrained and can’t just plug a camera into a wall,” Alexander explained. “For example, it could be deployed in field settings with limited power.”

“Separately, I also think it’s particularly exciting for applications like augmented reality where you’re interfacing with the physical world and need to know the locations of objects around you,” the researcher added.

The study’s co-first authors, Marcos Ferreira and Tianao Li, presented this work at the Computer Vision Foundation’s Conference on Computer Vision and Pattern Recognition (CVPR) in Denver. Complimentary registration is available for members of the media.

Christopher Plain is a Science Fiction and Fantasy novelist and Head Science Writer at The Debrief. Follow and connect with him on X, learn about his books at plainfiction.com, or email him directly at christopher@thedebrief.org.

Electric vehicles and other clean energy devices rely on batteries that use a metal called lithium. The expanded use of lithium has made lithium mining a rapidly growing industry. Lithium is most often found in volcanic rocks called pegmatites. Researchers have studied how much carbon and water pegmatite mining uses, but they don’t know how it affects local water quality. 

Pegmatite mining releases not only lithium but also other metals commonly found in pegmatite, such as rubidium and cesium. These trace metals are under-researched in terms of their environmental effects and possible human toxicity, though the EPA has linked high levels of lithium to adverse health effects in kidney function, neurological development, and thyroid function. As such, there’s currently no established safe level for lithium in drinking water, but the U.S. Geological Survey suggests a preliminary safety level of 10 micrograms per liter (μg/L). 

Researchers at Duke University recently tested how much pegmatite mining increased lithium levels in local waters, and how long those effects lingered after mining processes ended. To do so, they measured metal concentrations in waters downstream from 2 lithium mines and adjacent processing plants. They hypothesized that mining activities would increase interactions between rocks and the surrounding surface water and groundwater, impacting their lithium concentrations.

The researchers collected 99 water samples from surface streams and 93 samples from groundwater wells surrounding the Kings Mountain and Hallman Beam lithium mines, in a zone of about 40 kilometers (25 miles) along the border of South Carolina and North Carolina. The researchers took 51 surface water samples upstream of the mines to evaluate natural metal levels, and compared these with 48 downstream samples showing the mining-impacted metal levels. 

The researchers measured lithium and other trace elements, like rubidium, cesium, arsenic, and strontium, in the waters using an instrument called an inductively coupled plasma mass spectrometer. They found that lithium concentrations in the surface waters increased from a background of 0.2 to 79 μg/L to concentrations of 785 to 1,249 μg/L within 10 kilometers (6.2 miles) of the mines and processing facilities. The groundwater lithium levels were as high as 4,500 to 47,000 μg/L in the wells at the mines, but were not affected at downstream wells where lithium levels ranged from 0.5 to 890 μg/L. 

They suggested that lithium in the groundwater downstream from the mines came from natural interactions of the pegmatite rocks with the water, rather than from the mines themselves. This natural process was confirmed when the researchers noticed that lithium levels in the groundwater rose after rainfall, which caused more water-rock interaction.

The researchers also measured ions like calcium, sulfate, and chloride in the waters using a tool that can analyze compound molecules, called an ion chromatograph. They found that calcium and sulfate concentrations increased in surface waters within 10 kilometers (6.2 miles) downstream of the processing facilities. These downstream waters had 50 to 120 milligrams per liter (mg/L) of calcium and 100 to 300 mg/L of sulfate, compared to 5 to 20 mg/L of calcium and 3 to 10 mg/L of sulfate in the background surface waters. These ions are also byproducts of pegmatite processing, produced from a waste product made up of calcium sulfate, called gypsum. 

The team explained that active mining has been discontinued at this site for approximately 3 decades, meaning that the lithium concentrations they measured reflect the long-term release of lithium from inactive mines and mining waste. Drawing from historical data, they estimated that while the mines were active, they likely released 10 to 30 times the amount of trace metals they measured at the same location today. 

The researchers concluded that hard-rock processing impacts downstream metal and ion concentrations more than the mining itself. As lithium mining operations expand, the researchers suggested that future researchers examine lithium toxicity as well as the potential effects of its co-occurring metals, like rubidium and cesium. They also emphasized the need to find solutions for removing the trace metals and dissolved gypsum from water systems, or preventing them from leaking into the water systems to begin with.

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