Cost

As of my knowledge cutoff in September 2021, you could expect to pay roughly the following amounts for these components:

• Lawnmower or Chassis: This price can vary wildly depending on what you're looking for. A basic push mower might cost you around $150.

• Drive System (DC Motors): Depending on the power you need, motors can range from $20 to $100 each.

• Motor Drivers: Around $20 each.

• Raspberry Pi 4: About $35.

• GPS Module: Around $40.

• LiDAR Sensor: A basic model can start from around $100 but can go up to $1000 or more for more advanced models.

• High-Quality Camera: A basic model can be found for as low as $30, but a higher quality one can go up to $100 or more.

• Battery System: Depending on the power needed, you might spend between $50 and $200.

• Communication Module: A Wi-Fi module can be found for around $10.

Please note that prices can change and you should check the current prices from reliable vendors before purchasing.

Now, regarding the use of a camera for grass detection:

• The camera is connected to the Raspberry Pi, which acts as the 'brain' of the mower. It captures images that are processed in real-time, which can be done using a deep learning model. The model should be trained to recognize different objects, such as grass, concrete, driveways, pools, house edges, etc.

• Google Lens is a proprietary technology, and as of my last update in 2021, there's no available public API for Google Lens, which means you can't directly integrate it into your own projects. However, you can use other machine learning platforms like TensorFlow or PyTorch along with OpenCV to create and train your own image recognition model.

• Training an image recognition model requires a large dataset of labeled images. For example, you would need many pictures of grass, each labeled as 'grass', and similarly for each other object type. Then, you can use these images to train your model to recognize these objects in the real world.

• Once trained, the model can be deployed onto the Raspberry Pi to perform real-time image recognition. The lawnmower can then use this information to decide when to cut grass and when to avoid objects. The complexity of this task should not be underestimated – it requires a good understanding of machine learning and computer vision.