# US 2021/0241471 A1 · Adaptive feature map anchor pruning > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0241471 A1 - Document ID: US20210241471A1 - Status: Published application - Kind: A1 - Application number: 16/781,220 - Filed: 2020-02-04 - Published: 2021-08-05 - Earliest claimed: 2020-02-04 - Assignees: NIO USA, Inc. - Applicants: NIO USA, Inc. - Inventors: Zachary M. Kendrick; Disha Ahuja; Ashwath Aithal - Web page: https://patentgrep.com/patent/US20210241471A1 ## CPC - G01S 7/4808: {Evaluating distance, position or velocity data} - G06T 7/50: Depth or shape recovery - G01C 21/26: specially adapted for navigation in a road network - G01S 17/89: for mapping or imaging - G01S 17/931: of land vehicles - G06T 5/20: using local operators - G06T 5/70: Denoising; Smoothing - G06T 7/73: using feature-based methods - G01C 21/3826: {Terrain data} - G06T 2207/10028: Range image; Depth image; 3D point clouds - G06T 2207/20164: Salient point detection; Corner detection - G06T 2207/20216: Image averaging - G06T 2207/30252: Vehicle exterior; Vicinity of vehicle - G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S) ## Abstract Embodiments include a method for pruning anchor points from a feature map generated from a Light Detections And Ranging (LiDAR) point cloud, the method comprising: receiving, by a navigation system, a LiDAR point cloud from a LiDAR sensor, the LiDAR point cloud comprising data representing one or more objects in physical surroundings detected by the LiDAR sensor; extracting, by the navigation system, a feature map from the LiDAR point cloud, the feature map comprising a plurality of anchor points, each anchor point defined by an anchor box; smoothing, by the navigation system, the extracted feature map; determining, by the navigation system, density of pixels within the anchor box of each anchor point; and pruning, by the navigation system, anchor points from the feature map based on a plurality of factors related to the determined density of pixels within the box of each anchor point. ## Claims ### Claim 1 A method for pruning anchor points from a feature map generated from a Light Detections And Ranging (LiDAR) point cloud, the method comprising: receiving, by a navigation system, a LiDAR point cloud from a LiDAR sensor, the LiDAR point cloud comprising data representing one or more objects in physical surroundings detected by the LiDAR sensor; extracting, by the navigation system, a feature map from the LiDAR point cloud, the feature map comprising a plurality of anchor points, each anchor point defined by an anchor box; smoothing, by the navigation system, the extracted feature map; determining, by the navigation system, density of pixels within the anchor box of each anchor point; and pruning, by the navigation system, anchor points from the feature map based on a plurality of factors related to the determined density of pixels within the box of each anchor point. ### Claim 2 (depends on 1) The method of claim 1, wherein smoothing the extracted feature map comprises applying a Gaussian smoothing to the extracted feature map. ### Claim 3 (depends on 1) The method of claim 1, wherein determining the density of pixels within the anchor box of each anchor point comprises computing a sum of pixels within the anchor box of each anchor point. ### Claim 4 (depends on 3) The method of claim 3, wherein determining the density of pixels within the anchor box of each anchor point further comprises computing an average pixel value for the anchor box of each anchor point. ### Claim 5 (depends on 4) The method of claim 4, wherein determining the density of pixels within the anchor box of each anchor point further comprises computing a center pixel value for the anchor box of each anchor point. ### Claim 6 (depends on 5) The method of claim 5, wherein pruning anchor points from the feature map based on the plurality of factors related to the determined density of pixels within the box of each anchor point comprises pruning anchor points having an anchor box with an average pixel value below a predetermined threshold for average pixel value. ### Claim 7 (depends on 6) The method of claim 6, wherein pruning anchor points from the feature map based on the plurality of factors related to the determined density of pixels within the box of each anchor point further comprises pruning anchor points having an anchor box with a center pixel value below a predetermined threshold for center pixel value. ### Claim 8 A navigation system comprising: a processor; and a memory coupled with and readable by the memory and storing therein a set of instructions which, when executed by the processor, causes the processor to prune anchor points from a feature map generated from a Light Detections And Ranging (LiDAR) point cloud by: receiving, by a navigation system, a LiDAR point cloud from a LiDAR sensor, the LiDAR point cloud comprising data representing one or more objects in physical surroundings detected by the LiDAR sensor; extracting, by the navigation system, a feature map from the LiDAR point cloud, the feature map comprising a plurality of anchor points, each anchor point defined by an anchor box; smoothing, by the navigation system, the extracted feature map; determining, by the navigation system, density of pixels within the anchor box of each anchor point; and pruning, by the navigation system, anchor points from the feature map based on a plurality of factors related to the determined density of pixels within the box of each anchor point. ### Claim 9 (depends on 8) The navigation system of claim 8, wherein smoothing the extracted feature map comprises applying a Gaussian smoothing to the extracted feature map. ### Claim 10 (depends on 8) The navigation system of claim 8, wherein determining the density of pixels within the anchor box of each anchor point comprises computing a sum of pixels within the anchor box of each anchor point. ### Claim 11 (depends on 10) The navigation system of claim 10, wherein determining the density of pixels within the anchor box of each anchor point further comprises computing an average pixel value for the anchor box of each anchor point. ### Claim 12 (depends on 11) The navigation system of claim 11, wherein determining the density of pixels within the anchor box of each anchor point further comprises computing a center pixel value for the anchor box of each anchor point. ### Claim 13 (depends on 12) The navigation system of claim 12, wherein pruning anchor points from the feature map based on the plurality of factors related to the determined density of pixels within the box of each anchor point comprises pruning anchor points having an anchor box with an average pixel value below a predetermined threshold for average pixel value. ### Claim 14 (depends on 13) The navigation system of claim 13, wherein pruning anchor points from the feature map based on the plurality of factors related to the determined density of pixels within the box of each anchor point further comprises pruning anchor points having an anchor box with a center pixel value below a predetermined threshold for center pixel value. ### Claim 15 A vehicle comprising: a Light Detection And Ranging (LiDAR) sensor; a navigation system coupled with the LiDAR sensor and comprising a processor and a memory coupled with and readable by the processor and storing therein a set of instructions which, when executed by the processor, causes the processor to prune anchor points from a feature map generated from a Light Detections And Ranging (LiDAR) point cloud by: receiving, by a navigation system, a LiDAR point cloud from a LiDAR sensor, the LiDAR point cloud comprising data representing one or more objects in physical surroundings detected by the LiDAR sensor; extracting, by the navigation system, a feature map from the LiDAR point cloud, the feature map comprising a plurality of anchor points, each anchor point defined by an anchor box; smoothing, by the navigation system, the extracted feature map; determining, by the navigation system, density of pixels within the anchor box of each anchor point; and pruning, by the navigation system, anchor points from the feature map based on a plurality of factors related to the determined density of pixels within the box of each anchor point. ### Claim 16 (depends on 15) The vehicle of claim 15, wherein smoothing the extracted feature map comprises applying a Gaussian smoothing to the extracted feature map. ### Claim 17 (depends on 15) The vehicle of claim 15, wherein determining the density of pixels within the anchor box of each anchor point comprises computing a sum of pixels within the anchor box of each anchor point. ### Claim 18 (depends on 17) The vehicle of claim 17, wherein determining the density of pixels within the anchor box of each anchor point further comprises computing an average pixel value for the anchor box of each anchor point. ### Claim 19 (depends on 18) The vehicle of claim 18, wherein determining the density of pixels within the anchor box of each anchor point further comprises computing a center pixel value for the anchor box of each anchor point. ### Claim 20 (depends on 19) The vehicle of claim 19, wherein pruning anchor points from the feature map based on the plurality of factors related to the determined density of pixels within the box of each anchor point comprises: pruning anchor points having an anchor box with an average pixel value below a predetermined threshold for average pixel value; and pruning anchor points having an anchor box with a center pixel value below a predetermined threshold for center pixel value.